Captured Video Data Research Articles

Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.

Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use.

A Novel Frame-Selection Metric for Video Inpainting to Enhance Urban Feature Extraction.

In our digitally driven society, advances in software and hardware to capture video data allow extensive gathering and analysis of large datasets. This has stimulated interest in extracting information from video data, such as buildings and urban streets, to enhance understanding of the environment. Urban buildings and streets, as essential parts of cities, carry valuable information relevant to daily life. Extracting features from these elements and integrating them with technologies such as VR and AR can contribute to more intelligent and personalized urban public services. Despite its potential benefits, collecting videos of urban environments introduces challenges because of the presence of dynamic objects. The varying shape of the target building in each frame necessitates careful selection to ensure the extraction of quality features. To address this problem, we propose a novel evaluation metric that considers the video-inpainting-restoration quality and the relevance of the target object, considering minimizing areas with cars, maximizing areas with the target building, and minimizing overlapping areas. This metric extends existing video-inpainting-evaluation metrics by considering the relevance of the target object and interconnectivity between objects. We conducted experiment to validate the proposed metrics using real-world datasets from Japanese cities Sapporo and Yokohama. The experiment results demonstrate feasibility of selecting video frames conducive to building feature extraction.

Video anomaly detection: A systematic review of issues and prospects

The increase in the deployment of surveillance camera in outdoor and indoor settings have resulted in a growing demand for intelligent systems that can accurately detect and recognize human actions as well as other entities of interest within the captured video data. Although, human action recognition is a well-established topic in computer vision, abnormal behaviour detection has recently received increased research attention. Several abnormal behaviour detection systems have been proposed over the years to ensure human safety. However, only a few comprehensive and systematic reviews report on the current state and future direction of video anomaly detection(VAD) research. This present effort aims to contribute a systematic and detailed review of current research and advances in the detection of anomalous actions and entities in videos. The review focuses on studies published between 2003 and 2023. During the literature selection process, 530 scholarly articles were identified and evaluated to showcase prevalent research trends, techniques, datasets, and frameworks within the realm of VAD This review aims to offer a comprehensive understanding of key areas of focus among researchers, provide resources for commonly used public datasets for evaluation and experimentation, and examine advancements and integration of network design to accommodate the needs for handling multimedia information. To sum up, this study highlights various potential opportunities and obstacles about the VAD domain.VAD has gained significant interest in recent years due to its potential applications in various domains such as security, surveillance, traffic monitoring, medical imaging among others. Areas of further research include, Anomaly detection in real-time, multi-camera anomaly detection, privacy preserving in VAD to mention a few. The study revealed some key trends and challenges in VAD that can guide future research direction.

Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture

Field manual labor behavior recognition is an important task that applies deep learning algorithms to industrial equipment for capturing and analyzing people’s behavior during field labor. In this study, we propose a field manual labor behavior recognition network based on an enhanced SlowFast architecture. The main work includes the following aspects: first, we constructed a field manual labor behavior dataset containing 433,500 fast-track frames and 8670 key frames based on the captured video data, and labeled it in detail; this includes 9832 labeled frames. This dataset provides a solid foundation for subsequent studies. Second, we improved the slow branch of the SlowFast network by introducing the combined CA (Channel Attention) attention module. Third, we enhanced the fast branch of the SlowFast network by introducing the ACTION hybrid attention module. The experimental results show that the recognition accuracy of the improved SlowFast network model with the integration of the two attention modules increases by 7.08%. This implies that the improved network model can more accurately locate and identify manual labor behavior in the field, providing a more effective method for problem solving.

Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy.

The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and disseminating that information via real-time, intraoperative decision-making. The objective of the present study was to examine the feasibility and accuracy of a novel computer vision algorithm for hysterectomy surgical step identification. This was a retrospective study conducted on surgical videos of laparoscopic hysterectomies performed in 277 patients in five medical centers. We used a surgical intelligence platform (Theator Inc.) that employs advanced computer vision and AI technology to automatically capture video data during surgery, deidentify, and upload procedures to a secure cloud infrastructure. Videos were manually annotated with sequential steps of surgery by a team of annotation specialists. Subsequently, a computer vision system was trained to perform automated step detection in hysterectomy. Analyzing automated video annotations in comparison to manual human annotations was used to determine accuracy. The mean duration of the videos was 103 ± 43 min. Accuracy between AI-based predictions and manual human annotations was 93.1% on average. Accuracy was highest for the dissection and mobilization step (96.9%) and lowest for the adhesiolysis step (70.3%). The results of the present study demonstrate that a novel AI-based model achieves high accuracy for automated steps identification in hysterectomy. This lays the foundations for the next phase of AI, focused on real-time clinical decision support and prediction of outcome measures, to optimize surgeon workflow and elevate patient care.

Unsupervised video anomaly detection in UAVs: a new approach based on learning and inference

In this paper, an innovative approach to detecting anomalous occurrences in video data without supervision is introduced, leveraging contextual data derived from visual characteristics and effectively addressing the semantic discrepancy that exists between visual information and the interpretation of atypical incidents. Our work incorporates Unmanned Aerial Vehicles (UAVs) to capture video data from a different perspective and to provide a unique set of visual features. Specifically, we put forward a technique for discerning context through scene comprehension, which entails the construction of a spatio-temporal contextual graph to represent various aspects of visual information. These aspects encompass the manifestation of objects, their interrelations within the spatio-temporal domain, and the categorization of the scenes captured by UAVs. To encode context information, we utilize Transformer with message passing for updating the graph's nodes and edges. Furthermore, we have designed a graph-oriented deep Variational Autoencoder (VAE) approach for unsupervised categorization of scenes, enabling the extraction of the spatio-temporal context graph across diverse settings. In conclusion, by utilizing contextual data, we ascertain anomaly scores at the frame-level to identify atypical occurrences. We assessed the efficacy of the suggested approach by employing it on a trio of intricate data collections, specifically, the UCF-Crime, Avenue, and ShanghaiTech datasets, which provided substantial evidence of the method's successful performance.

BioDigit MG: A Pilot Study Examining a Digital Technology for Quantitative Assessment and Remote Monitoring of Myasthenia Gravis (P7-8.005)

<h3>Objective:</h3> To develop a standardized, clinically-validated digital solution for rigorous, longitudinal assessment of ocular weakness and dysarthria in myasthenia gravis (MG), which can be applied remotely. <h3>Background:</h3> MG is a chronic autoimmune neuromuscular disease characterized by fluctuating weakness of skeletal muscle, which frequent affects ocular muscles and speech. Current assessments of myasthenic weakness require neurologic expertise, are time-consuming and can still be subjective. Additionally, measured at one point in time, assessments provide a snapshot of a patient’s MG, which may not adequately reflect the spectrum of fluctuating weakness. Ratings of ocular weakness and dysarthria are particularly subjective. These shortcomings of current outcome measures limit performance of clinical trials and provide a limited view of the patient’s clinical status. <h3>Design/Methods:</h3> We developed a tablet-based digital health solution that captures video data collected by patients and MG-specific patient-reported outcome measures. Ptosis and dysarthria are rated asynchronously. The tablet provides text instruction and audio prompts to the patient and data are uploaded to a cloud. Predetermined measures of success have been defined. <h3>Results:</h3> A pilot study with a target sample size of 20 individuals with MG is ongoing to determine the feasibility of using the proposed solution as a health monitoring platform for MG. Two MG experts will rate speech and video data for dysarthria and ptosis based on the Quantitative Myasthenia Gravis (QMG) scale. Association between the extracted speech and facial characteristics to clinical scores of dysarthria and ptosis will be investigated. <h3>Conclusions:</h3> Despite considerable recent advances in treatments for MG, rigorous, longitudinal tools for monitoring of its characteristic fluctuating weakness remain limited and represent an unmet need. The feasibility, validity and acceptability of the proposed digital solution will be determined for 20 patients with MG. If successful, this technology will be further developed and studied in a larger observational study. <b>Disclosure:</b> An immediate family member of Dr. Guidon has received personal compensation for serving as an employee of GE Healthcare. Dr. Guidon has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alexion. Dr. Guidon has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Momenta. Dr. Guidon has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Argenx. The institution of Dr. Guidon has received research support from MGFA. The institution of Dr. Guidon has received research support from Project Data Sphere. The institution of Dr. Guidon has received research support from MGNet. The institution of Dr. Guidon has received research support from MGNet. Dr. Guidon has received publishing royalties from a publication relating to health care. Dr. Mishra has received personal compensation for serving as an employee of Biosensics. Miss Lindsay has nothing to disclose. Mr. Mulukutla has received personal compensation for serving as an employee of BioSensics LLC. Dr. Vaziri has received personal compensation for serving as an employee of Biosensics.

Artificial intelligence and automation in endoscopy and surgery.

Modern endoscopy relies on digital technology, from high-resolution imaging sensors and displays to electronics connecting configurable illumination and actuation systems for robotic articulation. In addition to enabling more effective diagnostic and therapeutic interventions, the digitization of the procedural toolset enables video data capture of the internal human anatomy at unprecedented levels. Interventional video data encapsulate functional and structural information about a patient's anatomy as well as events, activity and action logs about the surgical process. This detailed but difficult-to-interpret record from endoscopic procedures can be linked to preoperative and postoperative records or patient imaging information. Rapid advances in artificial intelligence, especially in supervised deep learning, can utilize data from endoscopic procedures to develop systems for assisting procedures leading to computer-assisted interventions that can enable better navigation during procedures, automation of image interpretation and robotically assisted tool manipulation. In this Perspective, we summarize state-of-the-art artificial intelligence for computer-assisted interventions in gastroenterology and surgery.

Artificial Intelligence Vision Based Analysis and Key Steps Identification Feasibility Study in Laparoscopic Hysterectomies

<h3>Study Objective</h3> The analysis of surgical videos and key steps identification using artificial intelligence (AI) holds great promise for the future of healthcare. In this paper, we present a novel computer vision algorithm for surgical steps identification in laparoscopic hysterectomy. <h3>Design</h3> A retrospective analysis of surgical videos of laparoscopic hysterectomies. <h3>Setting</h3> Gynecological department in a tertiary hospital with an average of 6500 procedures a year. <h3>Patients or Participants</h3> 190 laparoscopic hysterectomies from September 2020 to April 2022. <h3>Interventions</h3> Artificial intelligence-driven surgical platform that uses advanced computer vision technology to capture video data during all surgeries, de-identify it, and upload it to a secure cloud infrastructure. <h3>Measurements and Main Results</h3> A total of 190 full-length hysterectomies videos were manually annotated with sequential steps of surgery. Of these, 115 cases served as a training dataset for algorithm development, 28 cases were used for internal validation, and 47 were used as a separate testing cohort for evaluating algorithm accuracy. Concordance between AI-enabled automated video analysis and manual human video annotation was 92%. Algorithm accuracy was highest for the vaginal vault closure step (97%) and lowest for the adhesiolysis step (71%). <h3>Conclusion</h3> A variety of AI applications are expanding in clinical systems, from databases to intraoperative video analysis. The unique nature of surgical practice puts surgeons in a strong position to contribute to the next phase of AI, focused on real-time clinical decision support designed to optimize surgeon workflow and patient care. This work validates the ability to annotate the different steps of hysterectomy using an AI vision-based algorithm. Automated surgical video analysis has immediate impact in this field and the implementation of such a system holds enormous promise for future surgical education and improvement.

TOURISM, NATURAL PROTECTED AREAS AND OPEN SOURCE GEOSPATIAL TECHNOLOGIES

Abstract. The Covid-19 outbreak has greatly impacted society behaviours fostering proximity tourism and valorising the social role of peri-urban natural protected areas as key locations for outdoor activities. FOSS and FOSS4G can play a critical role to support the value creation for these sites. This work evaluates its application in the context of two different protected areas for the creation of 3D digital products, the monitoring of touristic fluxes and the conduction of parks management activities. To this aim three solutions that copes with the mentioned aspects are presented and gaps, weakness and limitations evaluated. The investigated solutions consists in: the data workflow from survey to 3D rendering using Blender and GIS plugin; the touristic fluxes monitoring system based on a machine learning algorithm for image recognition from captured video data streams and istSOS; and finally the park assets management system which is based on PostGIS and OpenLayers.

Perceptions of safety culture and recording in the operating room: understanding barriers to video data capture.

Recording in the operating room is an important tool to help surgical teams improve their performance. This is becoming more feasible using the Operating Room Black Box, a comprehensive data capture platform. Operating room (OR) staff, however, may voice reasonable concerns as recording initiatives are implemented. The objective of this study was to assess pre-implementation attitudes of OR staff toward operative recording and explore the relationship of these attitudes to the themes of (1) safety culture, (2) impostor syndrome, and (3) privacy concerns. This cross-sectional survey study measured staff members' beliefs and opinions of operative recording and used three previously validated tools (safety attitudes questionnaire, clance impostor phenomenon scale, and dispositional privacy concern) to assess personal and professional factors. Concepts were correlated using Pearson's correlation coefficient. Forty-three staff members participated in this study, with a response rate of 45% (n = 43/96, 20/22 nurses, 9/11 gynecologists, 14/63 anesthesiologists). Opinions of operative data capture were generally positive (5-point Likert scale, mean = 3.81, SD = 0.91). Nurses tended to have more favorable opinions of the OR Black Box as compared to gynecologists and anesthesiologists, though this did not reach statistical significance (4.15 vs. 3.67 vs 3.43, p = 0.06). Impostor syndrome characteristics correlated with concerns about litigation related to recording (r = - 0.32, p = 0.04). There are personal and professional attributes of the OR team that impact perceptions of the OR Black Box and implications around privacy and litigation. Addressing these concerns may facilitate successful implementation of the OR Black Box and improve team communication and patient safety in the OR.

PUBLIC POLICY MEETS PUBLIC SURVEILLANCE

This research examines cases of several ‘smart cities’ deploying camera technologies, particularly those augmented by AI and video data capture. Constituent groups including technology companies, city government employees, and a variety of citizen groups both directly and indirectly shape the policies for using these systems and the data they produce, presaging how society might deal with the escalating presence of the Internet of Things. The social shaping of technology approach contributes to the research's conceptual foundations while a political economy perspective frames a consideration of the ethical dynamics in play. The study investigates how seven urban communities frame and articulate the values and dangers of such systems operating in networked environments. Using archival data from official documents, the press, local and State ordinances, the study concludes that police interests have been significant drivers of these systems, alongside new environmental and management promises for improved cost savings. Public engagement practices vary, but in general are either nonexistent or anemic. Oversight practices likewise are under-developed. provides a descriptive picture of how cities view and experience this surveillance infrastructure and highlights of the policy problems associated with the city surveillance systems.

SMART-CAM FOR WAREHOUSE

We are implementing a project for memory optimization for video surveillance with the help of human motion happening in the surrounding in the warehouse and also will be able to playback the recorded video which will only have the human motion recorded video and images will be click continuously after a fixed interval of time to get a view when there is human motion or no human motion images will be always clicked and stored. This system is based on IoT (Internet of Things). It has a Raspberry Pi (RPI), Camera module to detect human (thief) or detection of fire. Motion detection is a process of confirming a change in the position of an object relative to its surroundings or the change in the surroundings relative to an object. Generally, motion detection is useful in real-time or active surveillance systems. IN this paper the main focus is given to the processing of the captured video data to detect human motion in it. So that it is easy for the user to view images and videos whenever he wants. So this system is very useful to maintain a warehouse and memory optimization will be done.

A novel efficient algorithm for duplicate video comparison in surveillance video storage systems

Video surveillance system is playing an inevitable role in monitoring the world in uncountable and unimaginable aspects. There are numerous systems has been adopted to monitor. There are various application like forest monitoring system, border monitoring system, home security system, traffic monitoring in which the surveillance captured video data are stored for surveillance processing immediately or later. The stored video data are identical even for every minute, hour and this lead to need of more capacity of storage just for duplicate storage unintentionally. Some methods have been proposed to remove duplicate video in storage server. There must be good and efficient algorithm has to be offered. This work is proposed a novel duplicate video identification algorithm called Intelligent Duplicate Check Algorithm (IDCA) for removing duplicate video data without discarding any unique video data. The IDCA algorithm improvised duplicate identification considerable percentage and thus efficient duplicate free storage may be achieved.

A Novel Vision-Based Towing Angle Estimation for Maritime Towing Operations

The demand for non-powered facility towing is increasing with the development of large-scale offshore projects. It is of great interest for its safe operation to measure the state of the towing process in real time. This paper proposed a computer vision algorithm designed to measure the tug yawing during the towing operation by estimating the towing line angle. The geometrical projection of the towing line from 3D to 2D is described in detail. By fixing the camera at specific locations and simplifying the calculation procedure, the towing line angle in the 3D world can be estimated by the line angle in the image. Firstly, the sea–sky line is detected to estimate the rolling angle of the tug in the captured image. Then, the towing line angle is calculated by an image processing method. At the same time, the estimation of the towing angle is achieved through the captured video data analysis. Finally, field experiments were carried out and the results demonstrated that this method is suitable for real-time calculation of the towing angle during the towing operation.

Ethical and Legal Aspects of Ambient Intelligence in Hospitals

Ambient intelligence in hospitals is an emerging form of technology characterized by a constant awareness of activity in designated physical spaces and of the use of that awareness to assist health care workers such as physicians and nurses in delivering quality care. Recently, advances in artificial intelligence (AI) and, in particular, computer vision, the domain of AI focused on machine interpretation of visual data, have propelled broad classes of ambient intelligence applications based on continuous video capture. One important goal is for computer vision-driven ambient intelligence to serve as a constant and fatigue-free observer at the patient bedside, monitoring for deviations from intended bedside practices, such as reliable hand hygiene and central line insertions. While early studies took place in single patient rooms, more recent work has demonstrated ambient intelligence systems that can detect patient mobilization activities across 7 rooms in an ICU ward and detect hand hygiene activity across 2 wards in 2 hospitals. As computer vision–driven ambient intelligence accelerates toward a future when its capabilities will most likely be widely adopted in hospitals, it also raises new ethical and legal questions. Although some of these concerns are familiar from other health surveillance technologies, what is distinct about ambient intelligence is that the technology not only captures video data as many surveillance systems do but does so by targeting the physical spaces where sensitive patient care activities take place, and furthermore interprets the video such that the behaviors of patients, health care workers, and visitors may be constantly analyzed. This Viewpoint focuses on 3 specific concerns: (1) privacy and reidentification risk, (2) consent, and (3) liability.

DeepReS: A Deep Learning-Based Video Summarization Strategy for Resource-Constrained Industrial Surveillance Scenarios

The exponential growth in the production of video contents in different industries causes an urgent need for effective video summarization (VS) techniques, in order to get an optimal storage and preservation of key information in the video. Compared to other domains, industrial videos are more challenging to process, as they usually contain diverse and complex events, which make their online processing a difficult task. In this article, we introduce an online system for intelligent video capturing, coarse and fine redundancy removal, and summary generation. First, we capture video data through resource-constrained devices in an industrial Internet of Things network, equipped with vision sensors and apply coarse redundancy removal through the comparison of low-level features. Second, we transmit the resulting frames to the cloud for detailed analysis, where sequential features are extracted for the selection of candidate keyframes. Finally, we refine the candidate keyframes in order to discriminate those with maximum information as part of the summary. The key contributions of this article include the coarse and fine refining of video data implemented over resource-restricted devices and the presentation of important data in the form of a summary. Experiments 1 1 [Online]. Available: https://github.com/tanveer-hussain/DeepRes-Video-Summarization . over publicly available datasets evince a 0.3-unit increase in the F1 score when compared to state-of-the-art and with reduced time complexity. Furthermore, we provide convincing results on our newly created dataset in an industrial environment, which is made publicly available for the research community along with its labeled ground truth.

Video Completion and Simultaneous Moving Object Detection for Extreme Surveillance Environments

Since automated cleaning systems are less common in extreme surveillance environments, the accumulations of combustion fuels, dust, dirt, spider webs, etc., affect the visibility and clarity of the captured video data to different degrees resulting in incomplete (missing) video sequences. Grounded on this significant practical scenario, this letter proposes a scheme to concurrently complete the missing entries and detect moving objects with efficient background separation by formulating a single convex optimization problem developed and implemented in the tensor framework. The work is implemented as a unified scheme for concurrent video completion and moving object detection using twist spatio-temporal total variation to enhance the detection performance of the foreground while fitting the tensor nuclear norm minimization for efficient background separation with half thresholding applied on the tensor singular values. Moreover, the sparse variations that are part of the dynamic background are addressed using $l_{1/2}$ regularization. The formulated minimization problem is solved using the augmented Lagrangian method with an alternating direction strategy. The work also has computational benefits and the excellence of this method is revealed in the quantified performance evaluation against the compared approaches.

Exogenous progesterone for cannabis withdrawal in women: Feasibility trial of a novel multimodal methodology

BackgroundSex differences in cannabis use disorder (CUD) and its treatment have been identified. Women report more severe withdrawal and have shown worse treatment outcomes. Ovarian hormones are implicated in these differences and research suggests that exogenous progesterone may be an effective pharmacotherapy. MethodsThe current randomized, placebo-controlled, feasibility trial tested a novel multimodal methodology for administering exogenous progesterone during acute cannabis withdrawal. Eight heavy cannabis using women received micronized progesterone (200 mg bid) (n = 3) or matching placebo (n = 5) during the early follicular phase of their menstrual cycle over a 5-day study period while abstaining from cannabis. Laboratory visits (days 1 and 5) included biological and self-report assessments, while home-based procedures (days 2–4) included ambulatory assessments, video data capture and tele-drug testing, and biological assessments. Primary outcomes were medication adherence and salivary hormone levels, and the exploratory outcome was cannabis withdrawal severity. ResultsMedication adherence rates were high as assessed via self-report (100.0%) and video data capture (98.0%). Salivary progesterone levels differed between groups over time (p < 0.027) and the progesterone group achieved levels within the normal range during the luteal phase in healthy adults. All tele-drug tests were negative confirming cannabis abstinence and there was an indication (p = 0.07) of reduced cannabis craving among participants receiving progesterone. ConclusionMore effective and sex-based treatments for cannabis use disorder are needed. The current study provides a novel multimodal methodology with low participant burden for investigating new medications for cannabis withdrawal. Clinical trials of progesterone for cannabis withdrawal may be warranted.

Real-Time Surveillance Through Face Recognition Using HOG and Feedforward Neural Networks

Real-time security requirements continue to increase due to the occurrence of various suspicious activities in open and closed environments. Day-to-day security threats may seriously affect everyone lives. Many techniques have been introduced in this regard, but still some issues remain unaddressed. The work presented in this paper provides video surveillance with improved accuracy and less computational complexity. The most significant part of the system consists of face localization, detection and recognition. The system obtains underlined facial data through a video dataset or from the real-time environment. Subsequently, face/foreground and background keyframes are extracted from at hand captured video data. Finally, extracted facial image data is compared with the facial images in the database. In case no match is found with the existing data, a security alarm or signal is generated, alerting security personnel to take action. The proposed system is more accurate, has better performance, and is low cost compared with existing systems.