People Tracking Research Articles

Crowd Analysis Using Computer Vision Techniques

This article presents a survey on crowd analysis using computer vision techniques, covering different aspects such as people tracking, crowd density estimation, event detection, validation, and simulation. It also reports how related the areas of computer vision and computer graphics should be to deal with current challenges in crowd analysis.

Relation of walking behavior and visual function during the Graz Mobility Test

Abstract Purpose Epiretinal prostheses are developed to enhance the orientation of low vision patients and consequently their mobility. However to document visual progress repeated testing is necessary to eliminate learning effects. For this purpose we developed the Graz Mobility Test (GMT) and evaluated its suitability in low vision patients with retinitis pigmentosa. Aim of this study was to investigate the relation of visual function parameters and walking behavior of patients. Methods Low vision patients (n=16) with visual acuity (VA) ranging from light perception to 1.0 logMAR passed through a mobility test consisting of four different, structurally similar mazes with black obstacles. Each test was completed four to six times. A people tracking system and a specially designed computer program recorded head movements and the patient´s walking path. In addition passage time and number of contacts were recorded. VA was tested with the Modified Grating Test (MGT) and visual field (VF) was examined with the Goldmann perimeter. Results The subjects were divided into groups according to their VA. Patients with eccentric fields in general proved to have fewer contacts (mean: 3,4 contacts) due to their early detection of peripheral obstacles than those with less peripheral perception (mean: 9,7 contacs). Patients with better VA required less time and less head movements to pass the maze. Conclusion Recordings and measurement of visual function and formation into groups allowed us evaluation of individual coping strategies. Hence potential benefits of epiretinal prostheses could be evaluated by comparing pre‐ and postoperative values.

Improved 3D reconstruction in smart-room environments using ToF imaging

This paper presents the use of Time-of-Flight (ToF) cameras in smart-rooms and how this leads to improved results in segmenting the people in the room from the background and consequently better 3D reconstruction of foreground objects. A calibrated rig consisting of one Swissranger SR3100 Time-of-Flight range camera and a high resolution standard CCD camera is set in a smart-room containing five other standard cameras. A probabilistic background model is used to segment each view and a shape from silhouette volume is reconstructed. It is shown that the presence of the range camera gives ways of eliminating regional artifacts and therefore creating a more robust input for higher level applications such as people tracking or human motion analysis.

A Digital Security System with Door Lock System Using RFID Technology

RFID, Radio Frequency Identification is an inexpensive technology, can be implemented for several applications such as security, asset tracking, people tracking, inventory detection, access control applications. The main objective of this paper is to design and implement a digital security system which can deploy in secured zone where only authentic person can be entered. We implemented a security system containing door locking system using passive type of RFID which can activate, authenticate, and validate the user and unlock the door in real time for secure access. The advantage of using passive RFID is that it functions without a battery and passive tags are lighter and are less expensive than the active tags. A centralized system manages the controlling, transaction and operation task. The door locking system functions in real time as the door open quickly when user put their tag in contact of reader. The system also creates a log containing check-in and check-out of each user along with basic information of user. General Terms Ubiquitous computing, Security

A Layered Approach to People Detection in 3D Range Data

People tracking is a key technology for autonomous systems, intelligent cars and social robots operating in populated environments. What makes the task difficult is that the appearance of humans in range data can change drastically as a function of body pose, distance to the sensor, self-occlusion and occlusion by other objects. In this paper we propose a novel approach to pedestrian detection in 3D range data based on supervised learning techniques to create a bank of classifiers for different height levels of the human body. In particular, our approach applies AdaBoost to train a strong classifier from geometrical and statistical features of groups of neighboring points at the same height. In a second step, the AdaBoost classifiers mutually enforce their evidence across different heights by voting into a continuous space. Pedestrians are finally found efficiently by mean-shift search for local maxima in the voting space. Experimental results carried out with 3D laser range data illustrate the robustness and efficiency of our approach even in cluttered urban environments. The learned people detector reaches a classification rate up to 96% from a single 3D scan.

Tracking people and cars using 3D modeling and CCTV

The aim of this study was to find a method for the reconstruction of movements of people and cars using CCTV footage and a 3D model of the environment. A procedure is proposed, in which video streams are synchronized and displayed in a 3D model, by using virtual cameras. People and cars are represented by cylinders and boxes, which are moved in the 3D model, according to their movements as shown in the video streams. The procedure was developed and tested in an experimental setup with test persons who logged their GPS coordinates as a recording of the ground truth. Results showed that it is possible to implement this procedure and to reconstruct movements of people and cars from video recordings. The procedure was also applied to a forensic case. In this work we experienced that more situational awareness was created by the 3D model, which made it easier to track people on multiple video streams. Based on all experiences from the experimental set up and the case, recommendations are formulated for use in practice.

Electronic Tracking for People with Dementia Who Get Lost outside the Home: A Study of the Experience of Familial Carers

Purpose: The study aimed to elicit a description of GPS (global positioning system) tracking use in the care of people with dementia in domestic settings and to generate hypotheses about impact. Procedures: Users were recruited through a commercial provider. Qualitative interviews with 10 carers were completed to generate an in-depth description of how the devices were used and the perceived impact. A questionnaire was administered to ascertain sample characteristics. Findings: Most carers preferred to use tracking as a back-up to other strategies of management, particularly supervision by a carer and locked doors. In cases where the carers perceived the risk of harm from getting lost to be low, tracking was used to preserve the independence of the person with dementia. The carers reported that tracking gave them reassurance and also enhanced the sense of independence both for themselves and for the person with dementia. The poor reliability of the device was identified as a substantial limitation. Conclusion: Larger studies are needed to assess the safety and clinical value of GPS tracking. These should explore the views of people with dementia. Assessment tools are needed to assess suitability. Occupational therapy can play a pivotal role in this process of intervention design, assessment and evaluation.

A Bank of Unscented Kalman Filters for Multimodal Human Perception with Mobile Service Robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot’s perception and recognition of humans, providing a useful contribution for the future application of service robotics.

Multimodal identification and tracking in smart environments

We present a model for unconstrained and unobtrusive identification and tracking of people in smart environments and answering queries about their whereabouts. Our model supports biometric recognition based upon multiple modalities such as face, gait, and voice in a uniform manner. The key technical idea underlying our approach is to abstract a smart environment by a state transition system in which each state records a set of individuals who are present in various zones of the environment. Since biometric recognition is inexact, state information is inherently probabilistic in nature. An event abstracts a biometric recognition step, and the transition function abstracts the reasoning necessary to effect state transitions. In this manner, we are able to integrate different biometric modalities uniformly and also different criteria for state transitions. Fusion of biometric modalities is also supported by our model. We define performance metrics for a smart environment in terms of the concepts of `precision' and `recall'. We have developed a prototype implementation of our proposed concepts and provide experimental results in this paper. Our conclusion is that the state transition model is an effective abstraction of a smart environment and serves as a good basis for developing practical systems.

Keep on tracking – even where the GPS satellites don't shine

Researchers borrow satellite orientation technology to develop location sensors that could help track people in environments that GPS can't reach

Data association and occlusion handling for vision-based people tracking by mobile robots

This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets.

Families’ and Professional Caregivers’ Views of Using Advanced Technology to Track People With Dementia

in this study we examined the ethical aspects of the use of the Global Positioning Systems (GPS) to track people with dementia. The findings are based on qualitative data gathered from focus groups of family and professional caregivers. The most important theme was the need to balance patients' need for safety with the need to preserve their autonomy and privacy. The main potential benefit of the use of GPS was related to the peace of mind of the caregivers themselves. The findings also suggest that caregivers' views change according to the locus of responsibility of the caregivers for the safety of people with dementia. The caregivers give preference to patients' safety more than autonomy when they are responsible for the patients. When the patients are under the responsibility of other caregivers, they give preference to patients' autonomy more than their safety. Overall, the variety and the depth of the views of different stakeholders toward the use of electronic tracking for people with dementia presented in this article provide a meaningful contribution to the ethical debate on this topic.

People Tracking with UWB Radar Using a Multiple-Hypothesis Tracking of Clusters (MHTC) Method

This paper presents a method to track multiple moving humans using Ultra-Wideband (UWB) radar. UWB radar can complement other human tracking technologies, as it works well in poor visibility conditions. Our tracking approach is based on a point process interpretation of the multi-path UWB radar scattering model for moving humans. Based on this model, we present a multiple hypothesis tracking (MHT) framework for tracking the ranges and velocities of a variable number of moving human targets. The multi-target tracking (MTT) problem for UWB radar differs from traditional applications because of the complex multipath scattering observations per target. We develop an MHT framework for UWB radar-based multiple human target tracking, which can simultaneously solve the complex observation clustering and data association problems using Bayesian inference. We present experimental results in which a monostatic UWB radar tracks both individual and multiple human targets to estimate target ranges and velocities, even with changing numbers of targets across radar scans.

FRAGMENTED PARTICLES-BASED ROBUST OBJECT TRACKING WITH FEATURE FUSION

Tracking people or objects across multiple cameras and maintaining a track within a camera is a challenging task in applications such as video surveillance, traffic monitoring, event detection and recognition. Some of the major challenges while tracking a target are illumination/scale changes and partial occlusion. In this paper, we propose a novel tracking framework using particle filter to efficiently track an object within a camera and a blob-based target association scheme for tracking across cameras. The proposed particle filter tracking algorithm uses a fragment-based approach to model the target and track it by fusing color and gradient features. The proposed solution incorporates coarser level spatial information by fragmenting each particle and is shown to be useful for tracking under partial occlusion. A fast yet robust model update is employed to overcome illumination changes. Experimental results show (i) the robustness of the fragment-based tracking approach with respect to illumination/scale change and partial occlusion and (ii) tracking persons across two cameras.

Multi-model Hypothesis Group Tracking and Group Size Estimation

People tracking is essential for robots that are supposed to interact with people. The majority of approaches track humans in the vicinity of the robot independently. However, people typically form groups that split and merge. These group formation processes reflect social relations and interactions that we seek to recognize in this paper. To this end, we pose the group tracking problem as a recursive multi-hypothesis model selection problem in which we hypothesize over both, the partitioning of tracks into groups (models) and the association of observations to tracks (assignments). Model hypotheses that include split, merge, and continuation events are first generated in a data-driven manner and then validated by means of the assignment probabilities conditioned on the respective model. Observations are found by clustering points from a laser range finder and associated to existing group tracks using the minimum average Hausdorff distance. We further propose a method to estimate the number of people in the individual groups. Experiments with a mobile robot demonstrate that the approach is able to accurately recover social grouping of people with respect to the ground truth. The results also show that tracking groups is clearly more efficient than tracking people separately. Our system runs in real-time on a typical desktop computer.

People Tracking Based on Multi-regions Joint Particle Filters

摘要: 针对视频人体运动跟踪中的遮挡问题, 提出了一种基于多区域联合粒子滤波器的跟踪方法. 算法把人体划分为多个关键区域, 通过基于多区域无向图的联合运动模型, 构造联合粒子滤波器, 并运用区域关联的观测评估策略对目标状态进行联合预测, 从而完成遮挡情况下目标的跟踪. 实验结果表明, 与基于单区域粒子滤波的跟踪方法相比, 本文提出的算法在具有较长时间部分和全部遮挡的跟踪问题上, 取得了较好的实验结果. 关键词: 计算机视觉 / 目标跟踪 / 多区域 / 联合粒子滤波

Real-time accumulative computation motion detectors.

The neurally inspired accumulative computation (AC) method and its application to motion detection have been introduced in the past years. This paper revisits the fact that many researchers have explored the relationship between neural networks and finite state machines. Indeed, finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The article shows how to reach real-time performance after using a model described as a finite state machine. This paper introduces two steps towards that direction: (a) A simplification of the general AC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation in FPGA of such a designed AC module, as well as an 8-AC motion detector, providing promising performance results. We also offer two case studies of the use of AC motion detectors in surveillance applications, namely infrared-based people segmentation and color-based people tracking, respectively.

Abstracting People's Trajectories for Social Robots to Proactively Approach Customers

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> For a robot providing services to people in a public space such as a shopping mall, it is important to distinguish potential customers, such as window shoppers, from other people, such as busy commuters. In this paper, we present a series of abstraction techniques for people's trajectories and a service framework for using these techniques in a social robot, which enables a designer to make the robot proactively approach customers by only providing information about target local behavior. We placed a ubiquitous sensor network consisting of six laser range finders in a shopping arcade. The system tracks people's positions as well as their local behaviors, such as fast walking, idle walking, wandering, or stopping. We accumulated people's trajectories for a week, applying a clustering technique to the accumulated trajectories to extract information about the use of space and people's typical global behaviors. This information enables the robot to target its services to people who are walking idly or stopping. The robot anticipates both the areas in which people are likely to perform these behaviors as well as the probable local behaviors of individuals a few seconds in the future. In a field experiment, we demonstrate that this service framework enables the robot to serve people efficiently. </para>

Tracking in a Dense Crowd Using Multiple Cameras

Tracking people in a dense crowd is a challenging problem for a single camera tracker due to occlusions and extensive motion that make human segmentation difficult. In this paper we suggest a metho...

Real-time multiple people tracking for automatic group-behavior evaluation in delivery simulation training

This paper aims at generating an automated way to evaluate the team-behavior of trainees in a delivery simulation course using video-processing techniques with emphasis on multiple people tracking. The paper is composed of two interacting, but clearly separated stages: moving people segmentation and multiple people tracking. At people segmentation stage, the combination of the Gaussian Mixture Model (GMM) and the Dynamic Markov Random Fields (DMRF) technique helps to extract the foreground pixels. For a better extraction of the human silhouettes, the energy function of DMRF is extended with texture information. At multiple people tracking stage, we concentrate on solving human-occlusion problem caused by interacting persons based on silhouette data and a non-linear regression model. Our model effectively transfers the person location problem during the occlusion into the finding of the local maximum points on a smooth curve, so that visual persons in the partial or complete occlusion can still be precisely captured. We have compared our algorithm with two other popular tracking algorithms: mean-shift and particle-filter. Experimental results reveal that the correctness of our method is much higher than the mean-shift algorithm and slightly lower than a particle-filter, however, with the major benefit of being a factor of 10–15 faster in computing.