Person Re-Identification in Multi-Camera Networks

Abstract

In this paper, we present an approach for person re-identification in multi-camera networks. This approach employs the Implicit Shape Model and SIFT features for person re-identification. One important property of the re-identification approach is that it is closely coupled to a person detection and tracking and uses SIFT feature models which are built during the tracking. We hold this coupling to be an important point because re-identification depends on models that are to be acquired during tracking. These models are then used to re-identify a person when it reappears in the system's field of view. Re-identification itself is performed in a 3-staged approach which allows for efficient re-identification and is perfectly suited for distributed processing where bandwidth concerns are relevant. We show that this re-identification approach - which was formerly only evaluated for single camera person re-identification can be successfully applied to the task of multi-camera re-identification. Evaluation in a challenging real-world multi-camera scenario shows that the generic approach which does not use color or other sensor specific features and thus is applicable independently of such sensor specifics - shows performance at least comparable to specialized state-of-the-art approaches.