Person Re-Identification Techniques for Intelligent Video Surveillance Systems

Nowadays, intelligent video-surveillance is one of the most active research fields in com- puter vision and machine learning techniques which provides useful tools for surveillance operators and forensic video investigators. Person re-identification is among these tools; it consists of recognizing whether an individual has already been observed over a network of cameras. This tool can also be employed in various possible applications, e.g., off-line retrieval of all the video-sequences showing an individual of interest whose image is given as query, or on-line pedestrian tracking over multiple cameras. For the off-line retrieval applications, one of the goals of person re-identification systems is to support video surveillance operators and forensic investigators to find an individual of interest in videos acquired by a network of non-overlapping cameras. This is attained by sorting images of previously ob- served individuals for decreasing values of their similarity with a given probe individual. This task is typically achieved by exploiting the clothing appearance, in which a classical biometric methods like the face recognition is impeded to be practical in real-world video surveillance scenarios, because of low-quality of acquired images. Existing clothing appearance descriptors, together with their similarity measures, are mostly aimed at im- proving ranking quality. These methods usually are employed as part-based body model in order to extract image signature that might be independently treated in different body parts (e.g. torso and legs). Whereas, it is a must that a re-identification model to be robust and discriminate on individual of interest recognition, the issue of the processing time might also be crucial in terms of tackling this task in real-world scenarios. This issue can be also seen from two different point of views such as processing time to construct a model (aka descriptor generation); which usually can be done off-line, and processing time to find the correct individual from bunch of acquired video frames (aka descriptor matching); which is the real-time procedure of the re-identification systems. This thesis addresses the issue of processing time for descriptor matching, instead of im- proving ranking quality, which is also relevant in practical applications involving interaction with human operators. It will be shown how a trade-off between processing time and rank- ing quality, for any given descriptor, can be achieved through a multi-stage ranking approach inspired by multi-stage approaches to classification problems presented in pattern recogni- tion area, which it is further adapting to the re-identification task as a ranking problem. A discussion of design criteria is therefore presented as so-called multi-stage re-identification systems, and evaluation of the proposed approach carry out on three benchmark data sets, using four state-of-the-art descriptors. Additionally, by concerning to the issue of processing time, typical dimensional reduction methods are studied in terms of reducing the processing time of a descriptor where a high-dimensional feature space is generated by a specific person re-identification descriptor. An empirically experimental result is also presented in this case, and three well-known feature reduction methods are applied them on two state-of-the-art descriptors on two benchmark data sets

Can we ID from CCTV? Image quality in digital CCTV and face identification performance

CCTV is used for an increasing number Of purposes, and the new generation of digital systems can be tailored to serve a wide range of security requirements. However, configuration decisions are often made without considering specific task requirements, e.g. the video quality needed for reliable person identification. Our Study investigated the relationship between video quality and the ability of untrained viewers to identify faces from digital CCTV images. The task required 80 participants to identify 64 faces belonging to 4 different ethnicities. Participants compared face images taken from a high quality photographs and low quality CCTV stills, which were recorded at 4 different video quality bit rates (32, 52, 72 and 92 Kbps). We found that the number of correct identifications decreased by 12 (similar to 18%) as MPEG-4 quality decreased from 92 to 32 Kbps, and by 4 (similar to 6%) as Wavelet video quality decreased from 92 to 32 Kbps. To achieve reliable and effective face identification, we recommend that MPEG-4 CCTV systems should be used over Wavelet, and video quality should not be lowered below 52 Kbps during video compression. We discuss the practical implications of these results for security, and contribute a contextual methodology for assessing CCTV video quality

Face recognition technologies for evidential evaluation of video traces

Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

Anonymous subject identification and privacy information management in video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework

Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/