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

Video analytics system for surveillance videos

Developing an intelligent inspection system that can enhance the public safety is challenging. An efficient video analytics system can help monitor unusual events and mitigate possible damage or loss. This thesis aims to analyze surveillance video data, report abnormal activities and retrieve corresponding video clips. The surveillance video dataset used in this thesis is derived from ALERT Dataset, a collection of surveillance videos at airport security checkpoints. The video analytics system in this thesis can be thought as a pipelined process. The system takes the surveillance video as input, and passes it through a series of processing such as object detection, multi-object tracking, person-bin association and re-identification. In the end, we can obtain trajectories of passengers and baggage in the surveillance videos. Abnormal events like taking away other's belongings will be detected and trigger the alarm automatically. The system could also retrieve the corresponding video clips based on user-defined query