Tracking people across disjoint camera views by an illumination-tolerant appearance representation

Tracking single individuals as they move across disjoint camera views is a challenging task since their appearance may vary significantly between views. Major changes in appearance are due to different and varying illumination conditions and the deformable geometry of people. These effects are hard to estimate and take into account in real-life applications. Thus, in this paper we propose an illumination-tolerant appearance representation, which is capable of coping with the typical illumination changes occurring in surveillance scenarios. The appearance representation is based on an online k-means colour clustering algorithm, a data-adaptive intensity transformation and the incremental use of frames. A similarity measurement is also introduced to compare the appearance representations of any two arbitrary individuals. Post-matching integration of the matching decision along the individuals' tracks is performed in order to improve reliability and robustness of matching. Once matching is provided for any two views of a single individual, its tracking across disjoint cameras derives straightforwardly. Experimental results presented in this paper from a real surveillance camera network show the effectiveness of the proposed method. © Springer-Verlag 2007

Linear color correction for multiple illumination changes and non-overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms

Contextual Information for Applications in Video Surveillance

With a growing network of cameras being used for security applications, video-based monitoring relying on human operators is ineffective and lacking in reliability and scalability. In this thesis, I present automatic solutions that enable monitoring of humans in videos, such as identifying same individuals across different cameras (human re-identification) and recognizing human activities. Analyzing videos using only individual-based features can be very challenging because of the significant appearance and motion variance due to the changing viewpoints, different lighting conditions, and occlusions. Motivated by the fact that people often form groups, it is feasible to model the interaction among group members to disambiguate the individual features in video analysis tasks. This thesis introduces features that leverage the human group as contextual information and demonstrates its performance for the tasks of human re-identification and activity recognition. Two descriptors are introduced for human re-identification. The Subject Centric Group (SCG) feature captures a person’s group appearance and shape information using the estimate of persons' positions in 3D space. The metric is designed to consider both human appearance and group similarity. The Spatial Appearance Group (SAG) feature extracts group appearance and shape information directly from video frames. A random-forest model is trained to predict the group's similarity score. For human activity recognition, I propose context features along with a deep model to recognize the individual subject’s activity in videos of real-world scenes. Besides the motion features of the person, I also utilize group context information and scene context information to improve the recognition performance. This thesis demonstrates the application of proposed features in both problems. Our experiments show that proposed features can reach state-of-the-art accuracy on challenging re-identification datasets that represent real-world scenario, and can also outperform state-of-the art human activity recognition methods on 5-activities and 6-activities versions of the Collective Activities dataset.Computer Science, Department o

Video analytics for security systems

This study has been conducted to develop robust event detection and object tracking algorithms that can be implemented in real time video surveillance applications. The aim of the research has been to produce an automated video surveillance system that is able to detect and report potential security risks with minimum human intervention. Since the algorithms are designed to be implemented in real-life scenarios, they must be able to cope with strong illumination changes and occlusions. The thesis is divided into two major sections. The first section deals with event detection and edge based tracking while the second section describes colour measurement methods developed to track objects in crowded environments. The event detection methods presented in the thesis mainly focus on detection and tracking of objects that become stationary in the scene. Objects such as baggage left in public places or vehicles parked illegally can cause a serious security threat. A new pixel based classification technique has been developed to detect objects of this type in cluttered scenes. Once detected, edge based object descriptors are obtained and stored as templates for tracking purposes. The consistency of these descriptors is examined using an adaptive edge orientation based technique. Objects are tracked and alarm events are generated if the objects are found to be stationary in the scene after a certain period of time. To evaluate the full capabilities of the pixel based classification and adaptive edge orientation based tracking methods, the model is tested using several hours of real-life video surveillance scenarios recorded at different locations and time of day from our own and publically available databases (i-LIDS, PETS, MIT, ViSOR). The performance results demonstrate that the combination of pixel based classification and adaptive edge orientation based tracking gave over 95% success rate. The results obtained also yield better detection and tracking results when compared with the other available state of the art methods. In the second part of the thesis, colour based techniques are used to track objects in crowded video sequences in circumstances of severe occlusion. A novel Adaptive Sample Count Particle Filter (ASCPF) technique is presented that improves the performance of the standard Sample Importance Resampling Particle Filter by up to 80% in terms of computational cost. An appropriate particle range is obtained for each object and the concept of adaptive samples is introduced to keep the computational cost down. The objective is to keep the number of particles to a minimum and only to increase them up to the maximum, as and when required. Variable standard deviation values for state vector elements have been exploited to cope with heavy occlusion. The technique has been tested on different video surveillance scenarios with variable object motion, strong occlusion and change in object scale. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster. Tracking objects in a multi camera environment is discussed in the final part of the thesis. The ASCPF technique is deployed within a multi-camera environment to track objects across different camera views. Such environments can pose difficult challenges such as changes in object scale and colour features as the objects move from one camera view to another. Variable standard deviation values of the ASCPF have been utilized in order to cope with sudden colour and scale changes. As the object moves from one scene to another, the number of particles, together with the spread value, is increased to a maximum to reduce any effects of scale and colour change. Promising results are obtained when the ASCPF technique is tested on live feeds from four different camera views. It was found that not only did the ASCPF method result in the successful tracking of the moving object across different views but also maintained the real time frame rate due to its reduced computational cost thus indicating that the method is a potential practical solution for multi camera tracking applications

Using latent features for short-term person re-identification with RGB-D cameras

This paper presents a system for people re-identification in uncontrolled scenarios using RGB-depth cameras. Compared to conventional RGB cameras, the use of depth information greatly simplifies the tasks of segmentation and tracking. In a previous work, we proposed a similar architecture where people were characterized using color-based descriptors that we named bodyprints. In this work, we propose the use of latent feature models to extract more relevant information from the bodyprint descriptors by reducing their dimensionality. Latent features can also cope with missing data in case of occlusions. Different probabilistic latent feature models, such as probabilistic principal component analysis and factor analysis, are compared in the paper. The main difference between the models is how the observation noise is handled in each case. Re-identification experiments have been conducted in a real store where people behaved naturally. The results show that the use of the latent features significantly improves the re-identification rates compared to state-of-the-art works.The work presented in this paper has been funded by the Spanish Ministry of Science and Technology under the CICYT contract TEVISMART, TEC2009-09146.Oliver Moll, J.; Albiol Colomer, A.; Albiol Colomer, AJ.; Mossi García, JM. (2016). Using latent features for short-term person re-identification with RGB-D cameras. Pattern Analysis and Applications. 19(2):549-561. https://doi.org/10.1007/s10044-015-0489-8S549561192http://kinectforwindows.org/http://www.gpiv.upv.es/videoresearch/personindexing.htmlAlbiol A, Albiol A, Oliver J, Mossi JM (2012) Who is who at different cameras. Matching people using depth cameras. Comput Vis IET 6(5):378–387Bak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using haar-based and dcd-based signature. 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Motion prediction and interaction localisation of people in crowds

PhDThe ability to analyse and predict the movement of people in crowded scenarios can be of fundamental importance for tracking across multiple cameras and interaction localisation. In this thesis, we propose a person re-identification method that takes into account the spatial location of cameras using a plan of the locale and the potential paths people can follow in the unobserved areas. These potential paths are generated using two models. In the first, people’s trajectories are constrained to pass through a set of areas of interest (landmarks) in the site. In the second we integrate a goal-driven approach to the Social Force Model (SFM), initially introduced for crowd simulation. SFM models the desire of people to reach specific interest points (goals) in a site, such as exits, shops, seats and meeting points while avoiding walls and barriers. Trajectory propagation creates the possible re-identification candidates, on which association of people across cameras is performed using spatial location of the candidates and appearance features extracted around a person’s head. We validate the proposed method in a challenging scenario from London Gatwick airport and compare it to state-of-the-art person re-identification methods. Moreover, we perform detection and tracking of interacting people in a framework based on SFM that analyses people’s trajectories. The method embeds plausible human behaviours to predict interactions in a crowd by iteratively minimising the error between predictions and measurements. We model people approaching a group and restrict the group formation based on the relative velocity of candidate group members. The detected groups are then tracked by linking their centres of interaction over time using a buffered graph-based tracker. We show how the proposed framework outperforms existing group localisation techniques on three publicly available datasets

A Non-Intrusive Multi-Sensor RGB-D System for Preschool Classroom Behavior Analysis

University of Minnesota Ph.D. dissertation. May 2017. Major: Computer Science. Advisor: Nikolaos Papanikolopoulos. 1 computer file (PDF); vii, 121 pages + 2 mp4 video filesMental health disorders are a leading cause of disability in North America and can represent a significant source of financial burden. Early intervention is a key aspect in treating mental disorders as it can dramatically increase the probability of a positive outcome. One key factor to early intervention is the knowledge of risk-markers -- genetic, neural, behavioral and/or social deviations -- that indicate the development of a particular mental disorder. Once these risk-markers are known, it is important to have tools for reliable identification of these risk-markers. For visually observable risk-markers, discovery and screening ideally should occur in a natural environment. However, this often incurs a high cost. Current advances in technology allow for the development of assistive systems that could aid in the detection and screening of visually observable risk-markers in every-day environments, like a preschool classroom. This dissertation covers the development of such a system. The system consists of a series of networked sensors that are able to collect data from a wide baseline. These sensors generate color images and depth maps that can be used to create a 3D point cloud reconstruction of the classroom. The wide baseline nature of the setup helps to minimize the effects of occlusion, since data is captured from multiple distinct perspectives. These point clouds are used to detect occupants in the room and track them throughout their activities. This tracking information is then used to analyze classroom and individual behaviors, enabling the screening for specific risk-markers and also the ability to create a corpus of data that could be used to discover new risk-markers. This system has been installed at the Shirley G. Moore Lab school, a research preschool classroom in the Institute of Child Development at the University of Minnesota. Recordings have been taken and analyzed from actual classes. No instruction or pre-conditioning was given to the instructors or the children in these classes. Portions of this data have also been manually annotated to create groundtruth data that was used to validate the efficacy of the proposed system