Automated Surveillance System Research Articles

Optimal deep transfer learning enabled object detector for anomaly recognition in pedestrian ways

Anomaly detection is a branch of behavior understanding in surveillance scenes, where anomalies represent a deviation in the behavior of scene entities (viz.,humans, vehicles, and environment) from regular patterns. In pedestrian walkways, this plays a vital role in enhancing safety. With the widespread use of video surveillance systems and the escalating video volume, manual examination of abnormal events becomes time-intensive.Hence, the need for an automated surveillance system adept at anomaly detection is crucial, especially within the realm of computer vision (CV) research. The surge in interest towards deep learning (DL) algorithms has significantly impacted CV techniques, including object detection and classification. Unlike traditional reliance on supervised learning requiring labeled datasets, DL offers advancements in these applications. Thus, this study presents an Optimal Deep Transfer Learning Enabled Object Detector for Anomaly Recognition in Pedestrian Ways (ODTLOD-ARPW) technique. The purpose of the ODTLOD-ARPW method is to recognize the occurrence of anomalies in pedestrian walkways using a DL-based object detector. In the ODTLOD-ARPW technique, the image pre-processing initially takes place using two sub-processes namely Wiener filtering (WF) based pre-processing and dynamic histogram equalization-based contrast enhancement. For anomaly detection, the ODTLOD-ARPW technique employs the YOLOV8s model which offers enhanced accuracy and performance. The hyperparameter tuning process takes place using a root mean square propagation (RMSProp) optimizer. The performance analysis of the ODTLOD-ARPW method is tested under the UCSD anomaly detection dataset. An extensive comparative study reported that the ODTLOD-ARPW technique reaches an effective performance with other models with maximum accuracy of 98.67%.

Automated surveillance of non-ventilator-associated hospital-acquired pneumonia (nvHAP): a systematic literature review

BackgroundHospital-acquired pneumonia (HAP) and its specific subset, non-ventilator hospital-acquired pneumonia (nvHAP) are significant contributors to patient morbidity and mortality. Automated surveillance systems for these healthcare-associated infections have emerged as a potentially beneficial replacement for manual surveillance. This systematic review aims to synthesise the existing literature on the characteristics and performance of automated nvHAP and HAP surveillance systems.MethodsWe conducted a systematic search of publications describing automated surveillance of nvHAP and HAP. Our inclusion criteria covered articles that described fully and semi-automated systems without limitations on patient demographics or healthcare settings. We detailed the algorithms in each study and reported the performance characteristics of automated systems that were validated against specific reference methods. Two published metrics were employed to assess the quality of the included studies.ResultsOur review identified 12 eligible studies that collectively describe 24 distinct candidate definitions, 23 for fully automated systems and one for a semi-automated system. These systems were employed exclusively in high-income countries and the majority were published after 2018. The algorithms commonly included radiology, leukocyte counts, temperature, antibiotic administration, and microbiology results. Validated surveillance systems' performance varied, with sensitivities for fully automated systems ranging from 40 to 99%, specificities from 58 and 98%, and positive predictive values from 8 to 71%. Validation was often carried out on small, pre-selected patient populations.ConclusionsRecent years have seen a steep increase in publications on automated surveillance systems for nvHAP and HAP, which increase efficiency and reduce manual workload. However, the performance of fully automated surveillance remains moderate when compared to manual surveillance. The considerable heterogeneity in candidate surveillance definitions and reference standards, as well as validation on small or pre-selected samples, limits the generalisability of the findings. Further research, involving larger and broader patient populations is required to better understand the performance and applicability of automated nvHAP surveillance.

Bi-Pose: Bidirectional 2D-3D Transformation for Human Pose Estimation From a Monocular Camera

Automatically estimating 3D human poses in video and inferring their meanings play an essential role in many human-centered automation systems. Existing researches made remarkable progresses by first estimating 2D human joints in video and then reconstructing 3D human pose from the 2D joints. However, mono-directionally reconstructing 3D pose from 2D joints ignores the interaction between information in 3D space and 2D space, losses rich information of original video, therefore limits the ceiling of estimation accuracy. To this end, this paper proposes a bidirectional 2D-3D transformation framework that bidirectionally exchanges 2D and 3D information and utilizes video information to estimate an offset for refining 3D human pose. In addition, a bone-length stability loss is utilized for the purpose of exploring human body structure to make the estimated 3D pose more natural and to further increase the overall accuracy. By evaluation, estimation error of the proposed method, measured by the mean per joint position error (MPJPE), is only 46.5 mm, which is much lower than state-of-the-art methods under the same experimental condition. The improvement on accuracy will make machines to better understand human poses for building superior human-centered automation systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the demand of human-centered automation systems needing to accurately understand human poses. Existing approaches mainly focus on inferring 3D human pose from 2D joints mono-directionally. Although they made remarkable contributions to estimating 3D human pose in such a mono-directional way, we found that they ignore the 2D-3D interaction and do not use original video when inferring 3D pose from 2D joints. This paper therefore suggests a bidirectional 2D-3D transformation that exchanges 2D and 3D information and utilizes video information to estimate more accurate 3D human pose for human-centered automation systems. This work is a pioneering attempt of interactively using 2D and 3D information for more accurate estimation of human pose. Benefited from the state-of-the-art accuracy, the proposed approach is expected to make significant contributions to many human-centered automation systems, such as human-machine interaction, biomimetic manipulation, and automatic surveillance systems.

Enhancing Anomaly Detection in Pedestrian Walkways using Improved Sparrow Search Algorithm with Parallel Features Fusion Model

Anomaly detection in pedestrian walkways is a vital research area, widely employed to enhance the safety of the pedestrians. Because of the widespread usage of the video surveillance systems and the increasing number of captured videos, the conventional manual examination of labeling abnormal events is a laborious process. Therefore, an automatic surveillance system to accurately detect anomalies becomes essential among computer vision researchers. Presently, the development of deep learning (DL) models has gained significant interest in different computer vision processes namely object classification and object detection, and these applications were depending on supervised learning that required labels. This article develops an Improved Meta-heuristic with Parallel Features Fusion Model for Anomaly Detection in Pedestrian Walkways (IMPFF-ADPW) method. The main aim of the IMPFF-ADPW approach is to recognize the existence of anomalies in pedestrian walkways. To obtain this, the IMPFF-ADPW method applies a joint bilateral filter (JBF) for the process of noise removal. Besides, a parallel fusion process comprising NasNet Mobile and Darknet-53 models can be utilized for feature extraction. For the anomaly detection method, the deep autoencoder (DAE) model is applied and its hyperparameters are finetuned by using an improved sparrow search algorithm (ISSA). A wide of experimental outcomes can be applied to the UCSD database to illustrate the betterment of the IMPFF-ADPW methodology. The simulation values indicated the enhanced performance of the IMPFF-ADPW method over other existing techniques.

Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways.

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches.

Automatic Video Traffic Surveillance System with Number Plate Character Recognition Using Hybrid Optimization-Based YOLOv3 and Improved CNN

Recently, the increased count of surveillance cameras has manipulated the demand criteria for a higher effective video coding process. Moreover, the ultra-modern video coding standards have appreciably enhanced the efficiency of video coding, which has been developed for gathering common videos over surveillance videos. Various vehicle recognition techniques have provided a challenging and promising role in computer vision applications and intelligent transport systems. In this case, most of the conventional techniques have recognized the vehicles along with bounding box depiction and thus failed to provide the proper locations of the vehicles. Moreover, the position details have been vigorous in terms of various real-time applications trajectory of vehicle’s motion on the road as well as movement estimation. Numerous advancements have been offered throughout the years in the traffic surveillance area through the random propagation of intelligent traffic video surveillance techniques. The ultimate goal of this model is to design and enhance intelligent traffic video surveillance techniques by utilizing the developed deep learning techniques. This model has the ability to handle video traffic surveillance by measuring the speed of vehicles and recognizing their number plates. The initial process is considered the data collection, in which the traffic video data is gathered. Furthermore, the vehicle detection is performed by the Optimized YOLOv3 deep learning classifier, in which the parameter optimization is performed by using the newly recommended Modified Coyote Spider Monkey Optimization (MCSMO), which is the combination of Coyote Optimization Algorithm (COA) and Spider Monkey Optimization (SMO). Furthermore, the speed of the vehicles has been measured from each frame. For high-speed vehicles, the same Optimized YOLOv3 is used for detecting the number plates. Once the number plates are detected, plate character recognition is performed by the Improved Convolutional Neural Network (ICNN). Thus, the information about the vehicles, which are violating the traffic rules, can be conveyed to the vehicle owners and Regional Transport Office (RTO) to take further action to avoid accidents. From the experimental validation, the accuracy and precision rate of the designed method achieves 97.53% and 96.83%. Experimental results show that the proposed method achieves enhanced performance when compared to conventional models, thus ensuring the security of the transport system.

Automated surveillance system for surgical site infections from hospital discharge letters

Abstract Background The surgical site infection (SSI) occurs in the part of the body where the surgery took place within 30 days. In Europe, 1-10% of surgical patients develop a SSI. Clinical reporting can play an important role in identifying SSIs, but these clinical data should be integrated into surveillance systems to increase their performances. We aim at developing an automated surveillance system to identify SSIs from unstructured text of hospital discharge letters (HDLs). Methods We extracted a sample of 2020-2021 HDLs from the University Hospital of Pisa (Italy) and we used a set of 60 keywords to select only HDLs including surgery and infection related terms. Using a positive wound swab test as a proxy for suspected SSI, we performed a record-linkage between HDLs and laboratory data for 2021. We compared the number of records with suspected SSIs before and after the filtering, to assess its sensitivity. Filtered HDLs have been manually labeled by qualified operators in 3 classes: SSI, No SSI, or SSI caused by a prior hospitalization. Results From 63,609 HDLs in our sample, 22,625 were filtered through keywords. In the pre-filtered dataset (only 2021), 255 patients have at least a positive wound swab test. 92% (235/255) of the patients with a suspected SSI were included after the filtering. In a preliminary analysis, after the labeling of 9,385 HDLs (41% of the dataset) we identified 340 SSIs (3,6%), of which 254 (85%) were caused by a prior hospitalization. Conclusions The labeled dataset will be used to train a natural language processing classification algorithm, able to identify suspected SSIs from HDLs. The keyword filtering can be a useful support to reduce the number of HDLs that need to be manually labeled. Key messages • Keyword filtering is the first step for an automated surveillance system of surgical site infections from hospital discharge letters. • Labeling is time-consuming but necessary for the development of a natural-language-processing-based surveillance system for surgical site infections.

Development and Evaluation of a Surveillance System for Follow-Up After Colorectal Polypectomy

The adherence of physicians and patients to published colorectal postpolypectomy surveillance guidelines varies greatly, and patient follow-up is critical but time consuming. To evaluate the accuracy of an automatic surveillance (AS) system in identifying patients after polypectomy, assigning surveillance intervals for different risks of patients, and proactively following up with patients on time. In this diagnostic/prognostic study, endoscopic and pathological reports of 47 544 patients undergoing colonoscopy at 3 hospitals between January 1, 2017, and June 30, 2022, were collected to develop an AS system based on natural language processing. The performance of the AS system was fully evaluated in internal and external tests according to 5 guidelines worldwide and compared with that of physicians. A multireader, multicase (MRMC) trial was conducted to evaluate use of the AS system and physician guideline adherence, and prospective data were collected to evaluate the success rate in contacting patients and the association with reduced human workload. Data analysis was conducted from July to September 2022. Assistance of the AS system. The accuracy of the system in identifying patients after polypectomy, stratifying patient risk levels, and assigning surveillance intervals in internal (Renmin Hospital of Wuhan University), external 1 (Wenzhou Central Hospital), and external 2 (The First People's Hospital of Yichang) test sets; the accuracy of physicians and their time burden with and without system assistance; and the rate of successfully informed patients of the system were evaluated. Test sets for 16 106 patients undergoing colonoscopy (mean [SD] age, 51.90 [13.40] years; 7690 females [47.75%]) were evaluated. In internal, external 1, and external 2 test sets, the system had an overall accuracy of 99.91% (95% CI, 99.83%-99.95%), 99.54% (95% CI, 99.30%-99.70%), and 99.77% (95% CI, 99.41%-99.91%), respectively, for identifying types of patients and achieved an overall accuracy of at least 99.30% (95% CI, 98.67%-99.63%) in the internal test set, 98.89% (95% CI, 98.33%-99.27%) in external test set 1, and 98.56% (95% CI, 95.86%-99.51%) in external test set 2 for stratifying patient risk levels and assigning surveillance intervals according to 5 guidelines. The system was associated with increased mean (SD) accuracy among physicians vs no AS system in 105 patients (98.67% [1.28%] vs 78.10% [18.01%]; P = .04) in the MRMC trial. In a prospective trial, the AS system successfully informed 82 of 88 patients (93.18%) and was associated with reduced burden of follow-up time vs no AS system (0 vs 2.86 h). This study found that an AS system was associated with improved adherence to guidelines among physicians and reduced workload among physicians and nurses.

A Framework for Personalized Human Activity Recognition

In today’s world, Human Activity Recognition (HAR) through video streams is actively used in every aspect of our life, such as automated surveillance systems and sports statistics are computed according to the videos with the help of HAR. Activity detection is not a new subject, and several methods are available. However, the most recent and most promising techniques rely on Convolutional Neural Networks (CNNs). CNNs primary usage is based on a single image frame to perform logical or categorical identification of an object, scene, or activity. We exploit this feature to adapt CNN on video streams to achieve HAR. In this study, we present a Personalized HAR (PHAR) framework that increases activity recognition accuracy with Object Detection (OD). First, we demonstrate the state-of-the-art HAR and OD methods in the literature. Then we illustrate our framework with two new Single Person Human Activity Recognition models. Finally, the performance of the new framework is evaluated with the well-known activity detection methods. Results show that our new PHAR model with 95% accuracy ratio outperforms the CNN-LSTM-based reference model (90%). Moreover, a new metric Average Accuracy Score (AAS) is described in this study, PHAR models approximately have 94% AAS, which is better than the reference model with 89% AAS.

Algorithmic State Violence: Automated Surveillance and Palestinian Dispossession in Hebron's Old City

AbstractThis article provides an ethnographic account of automated surveillance technologies' impact in the occupied West Bank, taking Blue Wolf—a biometric identification system deployed by the Israeli army—as a case study. Interviews with Palestinian residents of Hebron subjected to intensive surveillance, a senior Israeli general turned biometric start-up founder, and testimonies from veterans tasked with building up Blue Wolf's database provide a rare view into the uneven texture of life under algorithmic surveillance. Their narratives reveal how automated surveillance systems function as a form of state-sponsored terror. As a globalized information economy intersects with the eliminatory aims of Israeli settler colonialism in Hebron, new surveillance technologies erode Palestinian social life while allowing technocratic settlers to recast the violence of occupation as an opportunity for capital investment and growth. Attending to the texture of life under algorithmic surveillance in Hebron ultimately reorients theories of accumulation and dispossession in the digital age away from purely economistic framings. Instead, I foreground the violent political imperatives that drive innovations in surveillance, in Palestine and worldwide.

Computerized Ionospheric Tomography Based on the ADS-B System

The broadcast automatic dependent surveillance (ADS-B) system is a new-generation air traffic control system designed to avoid the waste of resources in secondary radars. The establishment of the spaceborne ADS-B system provides a broad prospect for ionospheric tomography. In this paper, the external observation information of the ionosphere is obtained by measuring the Faraday rotation angle, that is, the total electron content (TEC). Tomography research can be carried out all over the world to conduct large-scale ionospheric electron density research. The experiment selected two different regions and had a time resolution of two hours, a height resolution of 200 km, a latitude resolution of 2°, and a longitude resolution of 5°. Based on the simulated spaceborne ADS-B signal to invert the regional ionospheric electron density, the latitude, longitude, and height distributions of inversion result are basically consistent with those of the actual ionospheric electron density.

Validation of automated surveillance of healthcare-associated infections using electronic screening algorithms

Background: Surveillance of healthcare-associated infection (HAI) is the basis of infection prevention programs. However, manual review of medical records is a labor-intensive and time-consuming process. We evaluated the diagnostic performance of automated surveillance of HAI using electronic screening algorithms. Methods: Between April and June 2022, we conducted surveillance of HAI manually and automatically using electronic screening algorithm on 75 units (general medical and surgical wards and ICUs) in a 2,700-bed, tertiary-care hospital in South Korea. Algorithms for surveillance of HAI were developed accordance with NHSN surveillance definitions (Fig. 1). Catheter-associated urinary tract infections (CAUTIs) were automatically detected when eligible pathogen and fever (&gt;38°C) were matched within infection window period. Other specific types of infection were automatically classified based on laboratory results that met NHSN criteria. After the algorithm showed possible cases that met laboratory-confirmed bloodstream infection (LCBI) criteria, we excluded secondary BSIs using the automatic surveillance algorithm. We analyzed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the automated surveillance system compared to manual surveillance. Results: An algorithm for detecting CAUTI showed 98.7% sensitivity (78 of 79), 100.0% specificity (2,443 of 2,443), 100.0% PPV (78 of 78), and 100.0% NPV (2,443 of 2,444). For CLABSI, the algorithm had 97.3% sensitivity (214 of 220), 98.3% specificity (5,759 of 5,861), 67.7% PPV (214 of 316), and 99.9% NPV (5,759 of 5,765). In total, 102 cases of possible CLABSI were identified by the algorithm, and 76 (74.5%) were eventually diagnosed as secondary BSIs. Also, by chart review, 20 BSIs (19.6%) were present on arrival in ER (ER-POA). In 4 cases (3.9%), an original pathogen reoccurred in a repeated infection timeframe (RIT), and 2 cases (2%) were mucosal barrier injury-LCBI (MBI-LCBI). When we additionally performed manual surveillance for intra-abdominal infection secondary BSI, ER-POA, and assigning pathogen to original BSI in RIT, PPV increased to 87.7% (214 of 244). Conclusions: Algorithm for automated surveillance of CAUTI had good performance; however, automated surveillance of CLABSI was suboptimal. More elaborate screening algorithm for diagnosis CLABSI is needed, and further studies are needed to determine whether an automated surveillance system can reduce workload for surveillance of HAI.Disclosures: None

Prevention of surgical site infections: a personal odyssey

Prevention of surgical site infections: a personal odyssey

Clusters of healthcare-associated SARS-CoV-2 infections in Norwegian hospitals detected by a fully automatic register-based surveillance system

Notifications to the Norwegian Institute of Public Health of outbreaks in Norwegian healthcare institutions are mandatory by law, but under-reporting is suspected due to failure to identify clusters, or because of human or system-based factors. This study aimed to establish and describe a fully automatic, register-based surveillance system to identify clusters of healthcare-associated infections (HAIs) of SARS-CoV-2 in hospitals and compare these with outbreaks notified through the mandated outbreak system Vesuv. We used linked data from the emergency preparedness register Beredt C19, based on the Norwegian Patient Registry and the Norwegian Surveillance System for Communicable Diseases. We tested two different algorithms for HAI clusters, described their size and compared them with outbreaks notified through Vesuv. A total of 5033 patients were registered with an indeterminate, probable, or definite HAI. Depending on the algorithm, our system detected 44 or 36 of the 56 officially notified outbreaks. Both algorithms detected more clusters then officially reported (301 and 206, respectively). It was possible to use existing data sources to establish a fully automatic surveillance system identifying clusters of SARS-CoV-2. Automatic surveillance can improve preparedness through earlier identification of clusters of HAIs, and by lowering the workloads of infection control specialists in hospitals.

Image Dataset for Neural Network Performance Estimation with Application to Maritime Ports

Automated surveillance systems based on machine learning and computer vision constantly evolve to improve shipping and assist port authorities. The data obtained can be used for port and port property surveillance, traffic density analysis, maritime safety, pollution assessment, etc. However, due to the lack of datasets for video surveillance and ship classification in real maritime zones, there is a need for a reference dataset to compare the obtained results. This paper presents a new dataset for estimating detection and classification performance which provides versatile ship annotations and classifications for passenger ports with a large number of small- to medium-sized ships that were not monitored by the automatic identification system (AIS) and/or the vessel traffic system (VTS). The dataset is considered general for the Mediterranean region since many ports have a similar maritime traffic configuration as the Port of Split, Croatia. The dataset consists of 19,337 high-resolution images with 27,849 manually labeled ship instances classified into 12 categories. The vast majority of the images contain the port and starboard sides of the ships. In addition, the images were acquired in a real maritime zone at different times of the year, day, weather conditions, and sea state conditions.

Vehicle Smoke Synthesis and Attention-Based Deep Approach for Vehicle Smoke Detection

Third world countries are suffering from extreme vehicular air pollution due to dominating number of fossil fuel-driven vehicles on the road. Therefore, in these countries, automatic surveillance systems are in high demand for close monitoring to identify and penalize vehicles emitting excessive smoke. In recent times, deep learning-based computer vision systems are rigorously working on the same. Their accuracy strongly depends on the number of the training images taken under various imaging conditions. However, there are very few publicly available vehicle smoke datasets that could be used for training purposes. To capture on-road videos for the creation of a dataset is another challenging and time-consuming task. To aid and enhance the vehicular smoke monitoring system, in this article, we propose, a holistic dual-level framework for dataset enhancement by smoke generation along with a transformer network for efficient identification. We have created a realistic vehicle smoke generation algorithm using a range of mask patterns and filtering, which helps us to train our deep neural model by generating sufficient synthetic data. We have also proposed a transformer network on the YOLOv5 backbone, which efficiently identifies the smoke region and the smoky vehicle from the image frame simultaneously. We have shown that the lambda-implemented attention-based detection network outperforms the other state-of-the-art techniques on three baseline datasets. Sample demo videos are available at the link <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/srimantacse/VehicleSmoke</uri> .

Perspectives of Patients With Orthopedic Trauma on Fully Automated Digital Physical Activity Measurement at Home: Cross-sectional Survey Study.

The automated digital surveillance of physical activity at home after surgical procedures could facilitate the monitoring of postoperative follow-up, reduce costs, and enhance patients' satisfaction. Data on the willingness of patients with orthopedic trauma to undergo automated home surveillance postoperatively are lacking. The aims of this study were to assess whether patients with orthopedic trauma would be generally willing to use the proposed automated digital home surveillance system and determine what advantages and disadvantages the system could bring with it. Between June 2021 and October 2021, a survey among outpatients with orthopedic trauma who were treated at a European level 1 trauma center was conducted. The only inclusion criterion was an age of at least 16 years. The paper questionnaire first described the possibility of fully automated movement and motion detection (via cameras or sensors) at home without any action required from the patient. The questionnaire then asked for the participants' demographics and presented 6 specific questions on the study topic. In total, we included 201 patients whose mean age was 46.9 (SD 18.6) years. Most of the assessed patients (124/201, 61.7%) were male. Almost half of the patients (83/201, 41.3%) were aged between 30 and 55 years. The most stated occupation was a nine-to-five job (62/199, 30.8%). The majority of the participants (120/201, 59.7%) could imagine using the proposed measurement system, with no significant differences among the genders. An insignificant higher number of younger patients stated that they would use the automated surveillance system. No significant difference was seen among different occupations (P=.41). Significantly more young patients were using smartphones (P=.004) or electronic devices with a camera (P=.008). Less than half of the surveyed patients (95/201, 47.3%) stated that they were using tracking apps. The most stated advantages were fewer physician visits (110/201, 54.7%) and less effort (102/201, 50.7%), whereas the most prevalent disadvantage was the missing physician-patient contact (144/201, 71.6%). Significantly more patients with a part-time job or a nine-to-five job stated that data analysis contributes to medical progress (P=.047). Most of the assessed participants (120/201, 59.7%) stated that they would use the automated digital measurement system to observe their postoperative follow-up and recovery. The proposed system could be used to reduce costs and ease hospital capacity issues. In order to successfully implement such systems, patients' concerns must be addressed, and further studies on the feasibility of these systems are needed.

Robust Wheel Detection for Vehicle Re-Identification.

Vehicle re-identification is a demanding and challenging task in automated surveillance systems. The goal of vehicle re-identification is to associate images of the same vehicle to identify re-occurrences of the same vehicle. Robust re-identification of individual vehicles requires reliable and discriminative features extracted from specific parts of the vehicle. In this work, we construct an efficient and robust wheel detector that precisely locates and selects vehicular wheels from vehicle images. The associated hubcap geometry can hence be utilized to extract fundamental signatures from vehicle images and exploit them for vehicle re-identification. Wheels pattern information can yield additional information about vehicles in questions. To that end, we utilized a vehicle imagery dataset that has thousands of side-view vehicle collected under different illumination conditions and elevation angles. The collected dataset was used for training and testing the wheel detector. Experiments show that our approach could detect vehicular wheels accurately for 99.41% of the vehicles in the dataset.

Real‐time video anomaly detection for smart surveillance

AbstractHuman monitoring of surveillance cameras for anomaly detection may be a monotonous task as it requires constant attention to judge if the captured activities are anomalous or suspicious. This paper exploits background subtraction (BS), convolutional autoencoder, and object detection for a fully automated surveillance system. BS was performed by modelling each pixel as a mixture of Gaussians (MoG) to concatenate only the higher‐order learning in the foreground. Next, the foreground objects are fed to the convolutional autoencoders to filter out abnormal events from normal ones and automatically identify signs of threat and violence in real time. Then, object detection is introduced on the entire scene and the region of interest is highlighted with a bounding box to minimize human intervention in video stream processing. At recognition time, the network generates an alarm for the presence of an anomaly to notify of the identification of potentially suspicious actions. Finally, the complete system is validated upon several benchmark datasets and proved to be robust for complex video anomaly detection. The (AUC) average area under the curve for the frame‐level evaluation for all benchmarks is 94.94%. The best improvement ratio of AUC between the proposed system and state‐of‐the‐art methods is 7.7%.

A new automated national register-based surveillance system for outbreaks in long-term care facilities in Norway detected three times more severe acute respiratory coronavirus virus 2 (SARS-CoV-2) clusters than traditional methods.

To develop and test a new automated surveillance system that can detect, define and characterize infection clusters, including coronavirus disease 2019 (COVID-19), in long-term care facilities (LTCFs) in Norway by combining existing national register data. The numerous outbreaks in LTCFs during the COVID-19 pandemic highlighted the need for accurate and timely outbreak surveillance. As traditional methods were inadequate, we used severe acute respiratory coronavirus virus 2 (SARS-CoV-2) as a model to test automated surveillance. We conducted a nationwide study using data from the Norwegian preparedness register (Beredt C19) and defined the study population as an open cohort from January 2020 to December 2021. We analyzed clusters (≥3 individuals with positive SARS-CoV-2 test ≤14 days) by 4-month periods including cluster size, duration and composition, and residents' mortality associated with clusters. The study population included 173,907 individuals; 78% employees and 22% residents. Clusters were detected in 427 (43%) of 993 LTCFs. The median cluster size was 4-8 individuals (maximum, 50) by 4-month periods, with a median duration of 9-17 days. Employees represented 60%-82% of cases in clusters and were index cases in 60%-90%. In the last 4-month period of 2020, we detected 107 clusters (915 cases) versus 428 clusters (2,998 cases) in the last period of 2021. The 14-day all-cause mortality rate was higher in resident cases from the clusters. Varying the cluster definitions changed the number of clusters. Automated national surveillance for SARS-CoV-2 clusters in LTCFs is possible based on existing data sources and provides near real-time detailed information on size, duration, and composition of clusters. Thus, this system can assist in early outbreak detection and improve surveillance.