Agent-based framework for person re-identification

In computer based human object re-identification, a detected human is recognised to a level sufficient to re-identify a tracked person in either a different camera capturing the same individual, often at a different angle, or the same camera at a different time and/or the person approaching the camera at a different angle. Instead of relying on face recognition technology such systems study the clothing of the individuals being monitored and/or objects being carried to establish correspondence and hence re-identify the human object. Unfortunately present human-object re-identification systems consider the entire human object as one connected region in making the decisions about similarity of two objects being matched. This assumption has a major drawback in that when a person is partially occluded, a part of the occluding foreground will be picked up and used in matching. Our research revealed that when a human observer carries out a manual human-object re-identification task, the attention is often taken over by some parts of the human figure/body, more than the others, e.g. face, brightly colour shirt, presence of texture patterns in clothing etc., and occluding parts are ignored. In this thesis, a novel multi-agent based framework is proposed for the design of a human object re-identification system. Initially a HOG based feature extraction is used in a SVM based classification of a human object as a human of a full-body or of half body nature. Subsequently the relative visual significance of the top and the bottom parts of the human, in re-identification is quantified by the analysis of Gray Level Co-occurrence based texture features and colour histograms obtained in the HSV colour space. Accordingly different weights are assigned to the top and bottom of the human body using a novel probabilistic approach. The weights are then used to modify the Hybrid Spatiogram and Covariance Descriptor (HSCD) feature based re-identification algorithm adopted. A significant novelty of the human object re-identification systems proposed in this thesis is the agent based design procedure adopted that separates the use of computer vision algorithms for feature extraction, comparison etc., from the decision making process of re-identification. Multiple agents are assigned to execute different algorithmic tasks and the agents communicate to make the required logical decisions. Detailed experimental results are provided to prove that the proposed multi agent based framework for human object re-identification performs significantly better than the state of-the-art algorithms. Further it is shown that the design flexibilities and scalabilities of the proposed system allows it to be effectively utilised in more complex computer vision based video analytic/forensic tasks often conducted within distributed, multi-camera systems

People re-identification using depth and intensity information from an overhead sensor

This work presents a new people re-identification method, using depth and intensity images, both of them captured with a single static camera, located in an overhead position. The proposed solution arises from the need that exists in many areas of application to carry out identification and re-identification processes to determine, for example, the time that people remain in a certain space, while fulfilling the requirement of preserving people's privacy. This work is a novelty compared to other previous solutions, since the use of top-view images of depth and intensity allows obtaining information to perform the functions of identification and re-identification of people, maintaining their privacy and reducing occlusions. In the procedure of people identification and re-identification, only three frames of intensity and depth are used, so that the first one is obtained when the person enters the scene (frontal view), the second when it is in the central area of the scene (overhead view) and the third one when it leaves the scene (back view). In the implemented method only information from the head and shoulders of people with these three different perspectives is used. From these views three feature vectors are obtained in a simple way, two of them related to depth information and the other one related to intensity data. This increases the robustness of the method against lighting changes. The proposal has been evaluated in two different datasets and compared to other state-of-the-art proposal. The obtained results show a 96,7% success rate in re-identification, with sensors that use different operating principles, all of them obtaining depth and intensity information. Furthermore, the implemented method can work in real time on a PC, without using a GPU.Ministerio de Economía y CompetitividadAgencia Estatal de InvestigaciónUniversidad de Alcal

Tracklet and Signature Representation for Multi-shot Person Re-Identification.

International audienceVideo surveillance has become more and more important in many domains for their security andsafety. Person Re-Identification (Re-ID) is one of the most interesting subjects in this area. The Re-ID systemis divided into two main stages: i) extracting feature representations to construct a person’s appearance sig-nature and ii) establishing the correspondence/matching by learning similarity metrics or ranking functions.However, appearance based person Re-ID is a challenging task due to similarity of human’s appearance andvisual ambiguities across different cameras. This paper provides a representation of the appearance descriptors,called signatures, for multi-shot Re-ID. First, we will present the tracklets, i.e trajectories of persons. Then,we compute the signature and represent it based on the approach of Part Appearance Mixture (PAM). Anevaluation of the quality of this signature representation is also described in order to essentially solve the problemsof high variance in a person’s appearance, occlusions, illumination changes and person’s orientation/pose. Todeal with variance in a person’s appearance, we represent it as a set of multi-modal feature distributions modeledby Gaussian Mixture Model (GMM). Experiments and results on two public datasets and on our own datasetshow good performance

Multi-Object tracking using Multi-Channel Part Appearance Representation

International audienceAppearance based multi-object tracking (MOT) is a challenging task, specially in complex scenes where objects have similar appearance or are occluded by background or other objects. Such factors motivate researchers to propose effective trackers which should satisfy real-time processing and object trajectory recovery criteria. In order to handle both mentioned requirements, we propose a robust online multi-object tracking method that extends the features and methods proposed for re-identification to MOT. The proposed tracker combines a local and a global tracker in a comprehensive two-step framework. In the local tracking step, we use the frame-to-frame association to generate online object trajectories. Each object trajectory is called tracklet and is represented by a set of multi-modal feature distributions modeled by GMMs. In the global tracking step, occlusions and mis-detections are recovered by tracklet bipartite association method based on learning Mahalanobis metric between GMM components using KISSME metric learning algorithm. Experiments on two public datasets show that our tracker performs well when compared to state-of-the-art tracking algorithms

Computer vision methods applied to person tracking and identification

2013 - 2014Computer vision methods for tracking and identification of people in constrained and unconstrained environments have been widely explored in the last decades. De- spite of the active research on these topics, they are still open problems for which standards and/or common guidelines have not been defined yet. Application fields of computer vision-based tracking systems are almost infinite. Nowadays, the Aug- mented Reality is a very active field of the research that can benefit from vision-based user’s tracking to work. Being defined as the fusion of real with virtual worlds, the success of an augmented reality application is completely dependant on the efficiency of the exploited tracking method. This work of thesis covers the issues related to tracking systems in augmented reality applications proposing a comprehensive and adaptable framework for marker-based tracking and a deep formal analysis. The provided analysis makes possible to objectively assess and quantify the advantages of using augmented reality principles in heterogeneous operative contexts. Two case studies have been considered, that are the support to maintenance in an industrial environment and to electrocardiography in a typical telemedicine scenario. Advan- tages and drawback are provided as well as future directions of the proposed study. The second topic covered in this thesis relates to the vision-based tracking solution for unconstrained outdoor environments. In video surveillance domain, a tracker is asked to handle variations in illumination, cope with appearance changes of the tracked objects and, possibly, predict motion to better anticipate future positions. ... [edited by Author]XIII n.s

Multi-target tracking using appearance models for identity maintenance

This thesis considers perception systems for urban environments. It focuses on the task of tracking dynamic objects and in particular on methods that can maintain the identities of targets through periods of ambiguity. Examples of such ambiguous situations occur when targets interact with each other, or when they are occluded by other objects or the environment. With the development of self driving cars, the push for autonomous delivery of packages, and an increasing use of technology for security, surveillance and public-safety applications, robust perception in crowded urban spaces is more important than ever before. A critical part of perception systems is the ability to understand the motion of objects in a scene. Tracking strategies that merge closely-spaced targets together into groups have been shown to offer improved robustness, but in doing so sacrifice the concept of target identity. Additionally, the primary sensor used for the tracking task may not provide the information required to reason about the identity of individual objects. There are three primary contributions in this work. The first is the development of 3D lidar tracking methods with improved ability to track closely-spaced targets and that can determine when target identities have become ambiguous. Secondly, this thesis defines appearance models suitable for the task of determining the identities of previously-observed targets, which may include the use of data from additional sensing modalities. The final contribution of this work is the combination of lidar tracking and appearance modelling, to enable the clarification of target identities in the presence of ambiguities caused by scene complexity. The algorithms presented in this work are validated on both carefully controlled and unconstrained datasets. The experiments show that in complex dynamic scenes with interacting targets, the proposed methods achieve significant improvements in tracking performance

Design of a Multi-biometric Platform, based on physical traits and physiological measures: Face, Iris, Ear, ECG and EEG

Security and safety is one the main concerns both for governments and for private companies in the last years so raising growing interests and investments in the area of biometric recognition and video surveillance, especially after the sad happenings of September 2001. Outlays assessments of the U.S. government for the years 2001-2005 estimate that the homeland security spending climbed from $56.0 billions of dollars in 2001 to almost$100 billion of 2005. In this lapse of time, new pattern recognition techniques have been developed and, even more important, new biometric traits have been investigated and refined; besides the well-known physical and behavioral characteristics, also physiological measures have been studied, so providing more features to enhance discrimination capabilities of individuals. This dissertation proposes the design of a multimodal biometric platform, FAIRY, based on the following biometric traits: ear, face, iris EEG and ECG signals. In the thesis the modular architecture of the platform has been presented, together with the results obtained for the solution to the recognition problems related to the different biometrics and their possible fusion. Finally, an analysis of the pattern recognition issues concerning the area of videosurveillance has been discussed