On Using Gait Biometrics for Re-Identification in Automated Visual Surveillance

On Using Gait Biometrics for Re-Identification in Automated Visual Surveillance

Imed Bouchrika
DOI: 10.4018/978-1-5225-0703-1.ch008
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As surveillance becomes ubiquitous in such modern society due to the immense increase of crimes and the rise of terrorism activities, various government and military funded projects are devoted to research institutions to work on improving surveillance technology for the safety of their citizens. Because of the rapid growth of security cameras and impossibility of manpower to supervise them, the integration of biometric technologies into surveillance systems would be a critical factor for the automation of identity tracking over distributed cameras with disjoint views i.e. Re-Identification. The interest of using gait biometrics to re-identify people over networks of cameras emerges from the fact that the gait pattern can be captured and perceived at a distance as well as its non-invasive and less-intrusive nature.
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Although personal privacy has emerged as a major concern for the deployment of large scale surveillance systems, research into automated visual surveillance has received remarkable interest within the computer vision community with potential integration of biometric technologies and human activity recognition systems. This is mainly due to the proliferating number of crimes and terror attacks as well as the vital need to provide safer environment. In fact, the inability of human operators to monitor the increasingly growing numbers of CCTVs installed in highly sensitive and populated areas such as government buildings, airports or shopping malls, has rendered the usability of such systems to be useless. According to the British Security Industry Association, the number of surveillance cameras deployed in the UK was estimated to be more than 5 million in 2015; this figure is expected to increase rapidly particularly after the terrorist attacks that London witnessed in July 2005. Despite the huge increase of monitoring systems, the question whether current surveillance systems work as a deterrent to crime is still questionable. Security systems should not only be able to predict when a crime is about to happen but, more importantly, by early recognition of suspicious individuals who may pose security threats via the use of biometrics, the system would be able to deter future crimes as it is a significant requirement to identify the perpetrator of a crime as soon as possible in order to prevent further offences and to allow justice to be administered. The process of tracking people from one place to another place using surveillance networked cameras would be crucial for gathering valuable security intelligence. Moreover, queries can be made to search for possible locations of a given suspect that can indeed help security officers in their investigations and can lead to further evidence. Traditionally, it is impossible for human operators to work simultaneously on different video screens in order to track people of interest as well as analyze their behaviors across different places. Thus, it has become an essential requirement for research scientists from the computer vision community to investigate visual-based alternatives to automate the process for identity tracking over different views in addition to human activity analysis. Recently, various approaches were published in the literature to accomplish this task based on using basic features such as shape or color information. However, their practical deployment in real applications is very limited due to the complex nature of such problem. An alternative solution would be to employ biometric-based systems that can work at a distance and for low-resolution images such as gait and soft-based biometrics.

Key Terms in this Chapter

Soft Biometrics: A new type of people identification which concerns the use of simple semantic search attributes or terms that people use to describe each other linguistically.

Biometrics: The process concerned with extracting and deriving descriptive measurements based on either the human behavioral or physiological characteristics which should distinguish a subject uniquely among other people.

Gait Recognition: The biometric process to infer the person’s identity through the use of their gait style or pattern.

Gait Analysis: The systematic study of the human walking pattern which aims at the quantification and understanding of the locomotion process.

Identity Verification: The process is confirming the claimed identity of a person based on biometric matching against database records.

Gait: The manner of locomotion characterized by consecutive periods of loading and unloading the limbs. Gait includes running, walking and hopping.

Re-Identification: The process of recognizing whether an individual regardless of their true identity has already been observed over a network of cameras.

Camera Handover: The task of tracking and following people through identity matching between different surveillance cameras.

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