Person re-identification with local descriptors across multicameras.

Tracking the same person across multiple cameras is an important task in human motion analysis systems. It is also desirable to re-identify the individuals who have been previously seen with a single camera. This paper addresses this problem by the re-identification of the same individual in two different datasets, which are both challenging situations from human motion analysis systems. In this paper, local descriptors are introduced for image description, and support vector machines are employed for high classification performance and so an efficient bag-of-features approach for image presentation. In this way, robustness against low resolution, occlusion, and pose, viewpoint and illumination changes is achieved in a very fast way. We obtain promising results from the evaluation with situations where a number of individuals vary continuously from a multicamera system. [ABSTRACT FROM AUTHOR]