Clustering of tracklets for on-line multi-target tracking in networked camera systems

This paper considers the problem of tracking a variable number of objects through a surveillance site monitored by multiple cameras with slightly overlapping field-of-views. To this end, we propose to cluster tracklets generated by a commercially available single-camera video-analysis algorithm which is solely based on the position of objects. A first contribution of this paper is the proposal of a novel, extended energy function representing the confidence that two tracklets correspond to the same object. In contrast to previous work, the proposed motion-consistency error enables the clustering of tracklets from arbitrary views and temporal overlap. A second contribution is to evaluate the performance of several clustering algorithms. The results show that the clustering techniques employing only the merging of tracklets yield 10-15% higher F1 score than clustering techniques using various types of clustering moves including split and swap moves.