Abstract
A fundamental task for a distributed multi-camera system is to associate people across camera views at different locations and times. In a crowded and uncontrolled environment observed by cameras from a distance, person re-identification by biometrics such as face and gait is infeasible due to insufficient image details and arbitrary viewing conditions. Visual appearance features, extracted mainly from clothing, are intrinsically weak for matching people. For instance, most people in public spaces wear dark clothes in winter. A person’s appearance can also change significantly between different camera views if large changes occur in view angle, lighting, background clutter and occlusion. This results in different people appearing more alike than that of the same person across different camera views. In this chapter, we describe a method for learning the optimal matching distance criterion, regardless feature representation. This approach to person re-identification shifts the burden of computation from finding some universally optimal imagery features to discovering a matching mechanism for selecting adaptively different features that are locally optimal for each and every pairs of matches. Moreover, behaviour correlations hold useful spatio-temporal contextual information about expectations on where and when a person may re-appear in a networked visible space. This information is utilised for improving matching accuracy through context-aware search.
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Notes
- 1.
A probe image is an image of unknown identity.
- 2.
A gallery image is an image of known identity.
- 3.
A ‘probe’ is sometimes also referred to as a ‘query’.
- 4.
There were around 10% correct matches obtained from a gallery size of 532 people in the experiments carried out by Prosser et al. (2010).
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Gong, S., Xiang, T. (2011). Person Re-identification. In: Visual Analysis of Behaviour. Springer, London. https://doi.org/10.1007/978-0-85729-670-2_14
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DOI: https://doi.org/10.1007/978-0-85729-670-2_14
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