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Surveillance technology is now ubiquitous in modern society. This is due to the increasing number of crimes as well as the vital need to provide a safer environment. Because of the rapid growth of security cameras and incapability of manpower to supervise them, the deployment of biometric technologies becomes important for the development of an automated visual surveillance system. Recently, the use of gait for people identification in surveillance applications has attracted researchers from the arena of computer vision. The suitability of gait recognition for surveillance systems emerges from the fact that gait can be perceived from a distance as well as its non-invasive nature. Interestingly, in one of the high profile murder cases in the UK where a child was abducted and killed, the identity of the murderer could not be revealed directly from the surveillance video footage. The only solution that could be employed to determine the suspect’s identity in this situation was gait recognition, as proposed by researchers from the University of Southampton [Nixon, 2005]. However, gait as a biometric is still in its infancy and most of the gait recognition methods rely on body-related features.

Although people can discern the state of the subject from a single static image, motion pictures provide even more rich and reliable information for the perception of the different biological, social and psychological characteristics of the person such as emotions, actions and personality traits of the subject. This is because the acquired perceptual knowledge is encoded in the human motion. Furthermore, this notion was also observed by Darwin (1872) in his book “The Expression of Emotions in Man and Animals” where it was stated:

Actions speak louder than pictures when it comes to understanding what others are doing.

The human visual system is very sensitive to motion as it tends to focus attention on moving objects. In contrast to static or motionless objects, which are not as straightforward to detect. Motion is a spatio-temporal event defined as the change of spatial location over time. Given some visual input, the perception of motion is regarded as the process by which the visual system acquires perceptual knowledge about the speed and direction of the moving object. Whilst this process is spontaneous for the human visual system, it has proven to be extraordinarily difficult to duplicate this capability into computer vision systems.

In recent years, automatic visual surveillance  has received considerable interest in the computer vision community. This is due to the increasing numbers of crimes from robbery to terrorist attacks, as well as the inability of human operators to monitor the increasingly growing numbers of surveillance cameras deployed in security sensitive areas such as government buildings and airports, or  public places such as shopping malls and streets. According to the British Security Industry Association, the number of CCTV cameras installed in the UK was estimated to be more than 4.25 million in 2004; this figure is expected to grow rapidly particularly after the terrorist attacks that London witnessed in July 2005.  Despite the huge increase of surveillance systems,  the question whether current surveillance systems work as a deterrent to crime is still debatable. Security systems should not only be able to predict when a crime is about to happen but, more importantly, they ought to identify the individuals suspected of committing crimes, say through the use of biometrics such as gait recognition.

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