People Tracking Research Articles

Integrated roadmap for the rapid finding and tracking of people at large airports

This paper presents an integrated roadmap for the rapid finding and tracking of people at large airports. Business continuity at an airport is threatened by incidents such as left luggage and breaches of secure areas. These activities require rapid response because they are high frequency, high risk and high cost. For these reasons, live tracking and forensic searches of people are important tasks, for example, finding the owner of a left piece of luggage or tracking a trespasser. Finding and tracking people using surveillance cameras without technical aids is time consuming. The roadmap for the rapid finding and tracking of people at airports is based on discussions with end users, an assessment of the state-of-the-art and an integral assessment of work processes, human–machine interfaces, computer vision and the information and communications infrastructure. According to the roadmap, a major step forward can be achieved by integrating advancements in four areas: (i) increasing camera coverage; (ii) implementing computer vision for automated recognition and tracking; (iii) using contextual user interfaces; and (iv) adapting training and work processes to the tasks of finding and tracking people.

Time-of-flight-assisted Kinect camera-based people detection for intuitive human robot cooperation in the surgical operating room.

Scene supervision is a major tool to make medical robots safer and more intuitive. The paper shows an approach to efficiently use 3D cameras within the surgical operating room to enable for safe human robot interaction and action perception. Additionally the presented approach aims to make 3D camera-based scene supervision more reliable and accurate. A camera system composed of multiple Kinect and time-of-flight cameras has been designed, implemented and calibrated. Calibration and object detection as well as people tracking methods have been designed and evaluated. The camera system shows a good registration accuracy of 0.05m. The tracking of humans is reliable and accurate and has been evaluated in an experimental setup using operating clothing. The robot detection shows an error of around 0.04m. The robustness and accuracy of the approach allow for an integration into modern operating room. The data output can be used directly for situation and workflow detection as well as collision avoidance.

OpenPTrack: Open source multi-camera calibration and people tracking for RGB-D camera networks

OpenPTrack is an open source software for multi-camera calibration and people tracking in RGB-D camera networks. It allows to track people in big volumes at sensor frame rate and currently supports a heterogeneous set of 3D sensors.In this work, we describe its user-friendly calibration procedure, which consists of simple steps with real-time feedback that allow to obtain accurate results in estimating the camera poses that are then used for tracking people. On top of a calibration based on moving a checkerboard within the tracking space and on a global optimization of cameras and checkerboards poses, a novel procedure which aligns people detections coming from all sensors in a x-y-time space is used for refining camera poses.While people detection is executed locally, in the machines connected to each sensor, tracking is performed by a single node which takes into account detections from all over the network. Here we detail how a cascade of algorithms working on depth point clouds and color, infrared and disparity images is used to perform people detection from different types of sensors and in any indoor light condition.We present experiments showing that a considerable improvement can be obtained with the proposed calibration refinement procedure that exploits people detections and we compare Kinect v1, Kinect v2 and Mesa SR4500 performance for people tracking applications. OpenPTrack is based on the Robot Operating System and the Point Cloud Library and has already been adopted in networks composed of up to ten imagers for interactive arts, education, culture and human–robot interaction applications.

RFID-based Drug Management and Monitoring System, Case of Public Hospitals in Tanzania, A Review Paper

Radio frequency Identification (RFID) is an automatic identification technology that enables tracking of people and objects. Recently, RFID technology has been deployed in hospital environment for patient and equipment tracking, surgical equipment monitoring, medication monitoring and improving health record access in emergency cases. The main advantages RFID technology are to provide resource optimization, quality customers’ care, enhanced accuracy, efficient and effective business processes and healthcare processes. The pharmacy department undergoes challenges such as complex manual work of record keeping and inventory management. The RFID technology can be deployed in pharmacy hospital unit to automate pharmacy process. In this work we present a review on current pharmacy management practices in the case study of public hospital in Tanzania, review on different research work to address the pharmacy challenges and finally proposed a system to overcome the limitation identified in the current systems.

The Angel on Your Shoulder: Prompting Employees to Do the Right Thing Through the Use of Wearables

The wearable revolution is upon us. Bulky chest straps and large wristbands are going the way of flip cellphones and floppy disks. In the near future, for example, it may be commonplace for athletes to wear Biostamps or smart T-shirts with embedded sensors during practices, games, and even sleep. And while athletic competitors may have been one of the first movers in the area, health care, the military, and the industrial sector have all begun to use wearables to harness vast treasure troves of information destined to provide highly individualized feedback. The possibilities are almost endless when such personal information is combined with big data analytics in the name of improving large-scale efficiency.Interestingly, employers were one of the first movers in the wearable revolution. Yet, other than basic tracking of people and goods, there is still a tremendous potential for expansion. What if wearables could be harnessed to assist employees in avoiding conflict of interests? What if wearables could assist employees in identifying ethical dilemmas and could then prompt them to consider alternative courses of action? What if the wearable evolution became an ethical revolution?But the drawbacks of using wearables in such a manner must also be critically analyzed. This Article takes this step by exploring the use of wearables as personal information gathering devices that feed into larger data sets. It then considers some of the legal and policy implications of the use and aggregation of data in such a manner and ultimately makes suggestions for bottom-up baseline regulation. Ultimately, we argue for the desirability of leveraging this emerging technology, subject to privacy and security safeguards, to help drive an ethical revolution in business cultures.

Spatiotemporal Data Fusion using Dictionary Learning and Temporal Edge Primitives

The main target of stereo matching algorithms is to find out the three dimensional (3D) distance, or depth of objects from a stereo pair of images. Depth information can be derived from images using disparity map of the same scene. There are many applications of computer vision like People tracking, Gesture recognition, Industrial automation and inspection, Security and Biometrics, Three-dimensional modeling, Web and Cloud, Aerial surveys etc. There are large categories of stereo algorithms which are used for finding the disparity or depth. This paper presents a proposed stereo matching algorithm to obtain depth map, enhance and measure. The hybrid mathematical process of the algorithm are color conversion, block matching, guided filtering, Minimum disparity assignment design, mathematical perimeter, zero depth assignment, combination of hole filling and permutation of morphological operator and last non linear spatial filtering. Our algorithm is produce noise less, reliable, smooth and efficient depth map. We obtained the results with ground truth image using Structural Similarity Index Map (SSIM) and Peak Signal to Noise Ratio (PSNR). Index Terms—Stereo Matching; Disparity; Depth; Morphological operator; guided filter, Zero depth.

Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses

Monitoring pedestrian and cyclists movement is an important area of research in transport, crowd safety, urban design and human behaviour assessment areas. Media Access Control (MAC) address data has been recently used as potential information for extracting features from people’s movement. MAC addresses are unique identifiers of WiFi and Bluetooth wireless technologies in smart electronics devices such as mobile phones, laptops and tablets. The unique number of each WiFi and Bluetooth MAC address can be captured and stored by MAC address scanners. MAC addresses data in fact allows for unannounced, non-participatory, and tracking of people. The use of MAC data for tracking people has been focused recently for applying in mass events, shopping centres, airports, train stations, etc. In terms of travel time estimation, setting up a scanner with a big value of antenna’s gain is usually recommended for highways and main roads to track vehicle’s movements, whereas big gains can have some drawbacks in case of pedestrian and cyclists. Pedestrian and cyclists mainly move in built distinctions and city pathways where there is significant noises from other fixed WiFi and Bluetooth. Big antenna’s gains will cover wide areas that results in scanning more samples from pedestrians and cyclists’ MAC device. However, anomalies (such fixed devices) may be captured that increase the complexity and processing time of data analysis. On the other hand, small gain antennas will have lesser anomalies in the data but at the cost of lower overall sample size of pedestrian and cyclist’s data. This paper studies the effect of antenna characteristics on MAC address data in terms of travel-time estimation for pedestrians and cyclists. The results of the empirical case study compare the effects of small and big antenna gains in order to suggest optimal set up for increasing the accuracy of pedestrians and cyclists’ travel-time estimation.

Multisensor Information Fusion for People Tracking With a Mobile Robot: A Particle Filtering Approach

People tracking based on multisensor information fusion is addressed. A framework is presented for fusing the laser range finder (LRF) data and the monocular camera data. Based on this framework, an LRF-based detection algorithm is proposed to identify the pairs of human legs, by combining motion information and metric features. Moreover, a geometric observation model is developed for the camera to extract both the range and bearing measurements of the target person by focusing the target’s shoes with the camera. Then, a near-optimal particle filter is designed to fuse the measurements from the LRF and the camera. To prevent the sample impoverishment, a procedure of sample diversity improvement is used after the resampling step. The full occlusion problem is solved using image matching based on speeded up robust feature. Note that either of the LRF and the camera can work independently, since both the range and bearing are simultaneously acquired from the LRF or the camera. As a result, flexible and robust tracking can be achieved. Extensive experiments demonstrate that the proposed approach achieves high tracking accuracy and robustness. Especially, only a very small number of particles suffice to maintain good tracking performance.

An improvement on an MCMC-based video tracking algorithm

This paper presents an approach to fully automatic people tracking in surveillance video recorded by stable camera. We propose an improvement on Benfold et al. tracking-by-detection algorithm [1]. We extend the basic algorithm through filtering of person detector results and the scene entrance/exit positions construction. Moreover, the paper presents a modified method for tracklet position estimation. We compare several tracklet construction algorithms such as "Flock of Features" and normalized cross correlation. Our experiments reveal that all the proposed modifications improve both robustness and precision of tracks compared to the basic algorithm.

Sparsity-driven bandwidth-efficient decentralized tracking in visual sensor networks

Recent developments in low-cost CMOS cameras have created the opportunity of bringing imaging capabilities to sensor networks and a new field called visual sensor networks (VSNs) has emerged. VSNs consist of image sensors, embedded processors, and wireless transceivers which are powered by batteries. Since energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem. In this paper, we present a framework for human tracking in VSN environments. The traditional approach of sending compressed images to a central node has certain disadvantages such as decreasing the performance of further processing (i.e., tracking) because of low quality images. Instead, in our decentralized tracking framework, each camera node performs feature extraction and obtains likelihood functions. We propose a sparsity-driven method that can obtain bandwidth-efficient representation of likelihoods extracted by the camera nodes. Our approach involves the design of special overcomplete dictionaries that match the structure of the likelihoods and the transmission of likelihood information in the network through sparse representation in such dictionaries. We have applied our method for indoor and outdoor people tracking scenarios and have shown that it can provide major savings in communication bandwidth without significant degradation in tracking performance. We have compared the tracking results and communication loads with a block-based likelihood compression scheme, a decentralized tracking method and a distributed tracking method. Experimental results show that our sparse representation framework is an effective approach that can be used together with any probabilistic tracker in VSNs.

Theory of mind difficulties in patients with alcohol dependence: beyond the prefrontal cortex dysfunction hypothesis.

Previous studies have shown that alcohol-dependent (AD) individuals have difficulties inferring other people's emotion, understanding humor, and detecting a faux pas. This study aimed at further understanding the nature of such "Theory of Mind" (ToM) difficulties. A total of 34 recently detoxified AD and 34 paired controls were compared based on 2 nonverbal and video-based false belief tasks. These tasks were designed to identify 3 different types of deficits: (i) a deficit in dealing with the general task demands, (ii) a selective deficit in self-perspective inhibition, and (iii) a deficit in tracking the other person's mental state. (i) and (ii) are compatible with the hypothesis of a prefrontal cortex dysfunction being at the origin of AD individuals' social difficulties, while (iii) would suggest the possible contribution of a dysfunction of the temporo-parietal junction in explaining the social difficulties. Group analyses highlighted that AD individuals performed worse on the 2 false belief tasks than controls. Individual analyses showed, however, that just under half of the AD individuals were impaired compared to controls. Moreover, most of the AD individuals who were impaired showed a deficit in tracking the other person's belief. This deficit was linked to disease-related factors such as illness duration, average alcohol consumption, and craving but not to general reasoning abilities, depression, anxiety, or demographic variables. Just under half of the AD individuals tested showed a ToM deficit, and in most cases, the deficit concerned the tracking of other people's mental states. Such a type of deficit has previously been associated with lesions to the temporo-parietal brain areas, indicating that a prefrontal cortex dysfunction may not be the sole origin of the social cognition deficits observed in alcohol dependence.

Low Cost Real Time Location System Based in Radio Frequency Identification for the Provision of Social and Safety Services

The complexity of deployment and high implementation costs impede proliferation of real time location systems, depriving society of the benefits in terms of safety that these systems are capable of providing. The people tracking system presented in this article prioritizes ease of installation and adaptability to new low cost devices in emerging market against location accuracy to suit large scenarios that lack own infrastructure. The system has been implemented in a large playground to provide to the guardians of monitored children information about location and alert when an output of the controlled area is detected. Wireless channel characterization has been performed by means of deterministic 3D ray launching technique in order to assess the radioplanning process in the implemented network. This article describes the system and how it has been deployed in the validation scenario.

RARE: people detection in crowded passages by range image reconstruction

In this paper, we address the problem of people detection and tracking in crowded scenes using range cameras. We propose a new method for people detection and localisation based on the combination of background modelling and template matching. The method uses an adaptive background model in the range domain to characterise the scene without people. Then a 3D template is placed in possible people locations by projecting it in the background to reconstruct a range image that is most similar to the observed range image. We tested the method on a challenging outdoor dataset and compared it to two methods that each shares one characteristic with the proposed method: a similar template-based method that works in 2D and a well-known baseline method that works in the range domain. Our method performs significantly better, does not deteriorate in crowded environments and runs in real time.

Robert Love’s Warnings: Searching for Strangers in Colonial Boston by Cornelia H. Dayton and Sharon V. Salinge, and: Taming Lust: Crimes Against Nature in the Early Republic by Doron S. Ben-Atar and Richard D. Brown (review)

Reviewed by: Robert Love’s Warnings: Searching for Strangers in Colonial Boston by Cornelia H. Dayton and Sharon V. Salinge, and: Taming Lust: Crimes Against Nature in the Early Republic by Doron S. Ben-Atar and Richard D. Brown Thomas J. Humphrey (bio) Keywords Microhistories, Poor relief, Crime in early U.S., Early U.S. legal system Robert Love’s Warnings: Searching for Strangers in Colonial Boston. By Cornelia H. Dayton and Sharon V. Salinger. (Philadelphia: University of Pennsylvania Press, 2014. Pp. 272. Cloth, $34.95.) Taming Lust: Crimes Against Nature in the Early Republic. By Doron S. Ben-Atar and Richard D. Brown. (Philadelphia: University of Pennsylvania Press, 2014. Pp. 216. Cloth, $34.95.) Over the past forty years, historians have produced a veritable mountain of studies that may best be described as microhistories. When writing microhistories, historians often invoke a specific moment, or a few moments, from a subject’s life to reach some broader conclusions of that subject’s community.1 Two recent books provide excellent examples of microhistory. In Robert Love’s Warnings, Cornelia H. Dayton and Sharon V. Salinger study the copybooks and warnings issued by Robert Love to investigate the region’s mobile working population. In doing so, they uncover Boston’s civic, legal, social, and civil community in the last decade of the colonial period. In Taming Lust, Doron S. Ben-Atar and Richard D. Brown examine the lives of Gideon Washburn and John Farrell, two elderly men who, in the 1790s, were accused and convicted of bestiality. The trials and their aftermath reveal a New England in flux as residents struggled to reconcile their cultural and political traditions with the new realities of a post-Revolutionary United States. Both books use their specific subjects as windows to explore and explain the wider world these men inhabited. While Dayton and Salinger expertly detail the changes Boston’s mobile working class faced as the imperial crisis [End Page 313] intensified, Ben-Atar and Brown show us a New England in turmoil as the region’s residents tried to make sense of their new, post-Revolutionary world. Dayton and Salinger introduce readers to Love’s world by taking us on a walk. This is, however, no ordinary walk. Love was appointed to issue warnings to non-residents in Boston, a job he held from January 1765 until he died in April 1774. Thus, for roughly nine years, Robert Love spent approximately three days per week walking the streets of Boston, looking for people he needed to question. If the people he interrogated had no prospects for work or a place to live, he warned them that they had to leave the city in fourteen days. Few people saw transient and working-class people more regularly, or more closely, than did Robert Love. Love’s story of warning out is part of a larger English culture that colonists carried with them when they crossed the Atlantic. In the seventeenth century, England’s Parliament began passing a series of acts collectively termed the Poor Law that determined who was eligible for poor relief and how officials should provide it. People removed from town were ultimately sent back to their hometowns, where they might legally obtain some aid. In eighteenth-century Massachusetts, nonresidents could be warned and, if necessary, removed after authorities acquired a court order. While less onerous than the practice in England, Massachusetts used warning out as a way to minimize or eliminate the amount of poor relief paid to strangers. By the time Robert Love issued his warnings in 1765, Massachusetts officials were using warnings more as a way to keep track of people than as a way to get rid of the poor. Love kept track of his interactions in a series of notebooks, which Dayton and Salinger use to reveal the people who lived and worked in Boston in the last ten years of the colonial period. Because he relied on visual cues to remind him whom he had warned, Love often described people’s faces, hair, clothes, and accents. One man he described as middle aged with his ''own black Hair'' (60). Another was a sailor who had apparently deserted a British...

People Tracking in Camera Networks: Three Open Questions

Camera networks provide opportunities for practical video surveillance and monitoring, but tracking people across the network presents many computational and modeling hurdles that researchers have yet to surmount.

Tracking people via their cell phone battery usage

Tracking people via their cell phone battery usage

Simultaneous people tracking and robot localization in dynamic social spaces

Accurate robot localization and people tracking are necessary for deploying service robots in crowded everyday environments such as shopping malls, and features like product displays change over time, making map-based localization using on-board sensors difficult. We propose the use of an external sensor system to track people together with one or more robots. This approach is more robust to occlusions than on-board sensing and is unaffected by changing map features. In our system, laser range finders track people and robots in the environment, and odometry data is used to associate each robot with a tracked entity and correct the robot's pose. Techniques are also presented for identifying and recovering from tracking errors. Simulation results show that our system can outperform localization using on-board sensors, both in tracking accuracy and in automatic recovery from errors. We demonstrate our system's effectiveness in simulation, in a controlled experiment in a real shopping mall environment, and in real human---robot interactions with customers in a busy shopping arcade.

Detecting and Tracking of Multiple People in Video based on Hybrid Detection and Human Anatomy Body Proportion

This paper addresses problems of detection and tracking of moving multiple people in a video stream. Detecting and tracking are fundamental tasks for future research into Human Computer Interaction (HCI). Detecting and Tracking multiple people in video are considered time consuming processes due to the amount of data a video contains, illumination changes, complex backgrounds and occlusions that occur as soon as people change orientations over time. This study focus on developing a fully automated system aims to Detecting and tracking multiple people in video, by analyzes sequential video frames based on hybrid detection algorithm, and tracking based on human body structure. The performance of the proposed system is tested through a series of experiments and human computer interaction application based human detection, tracking and identification. Identification is based on new clustering method mentioned in this paper. General Terms Video Processing, Computer vision systems, Human detection and tracking, Clustering.

A multimodal approach for individual tracking of people and their belongings

In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object’s size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem.

Sleep apps and the quantified self: blessing or curse?

Sleep apps and the quantified self: blessing or curse?