How does person identity recognition help multi-person tracking?

We address the problem of multi-person tracking in a complex scene from a single camera. Although tracklet-association methods have shown impressive results in several challenging datasets, discriminability of the appearance model remains a limitation. Inspired by the work of person identity recognition, we obtain discriminative appearance-based affinity models by a novel framework to incorporate the merits of person identity recognition, which help multi-person tracking performance. During off-line learning, a small set of local image descriptors is selected to be used in on-line learned appearances-based affinity models effectively and efficiently. Given short but reliable track-lets generated by frame-to-frame association of detection responses, we identify them as query tracklets and gallery tracklets. For each gallery tracklet, a target-specific appearance model is learned from the on-line training samples collected by spatio-temporal constraints. Both gallery tracklets and query tracklets are fed into hierarchical association framework to obtain final tracking results. We evaluate our proposed system on several public datasets and show significant improvements in terms of tracking evaluation metrics.

1. Introduction

Tracking multiple people in a real scene is an important topic in the field of computer vision since it has many applications such as surveillance systems, robotics, and human-computer interaction environments. This is a highly challenging problem, especially in complex and crowded environments with frequent occlusions and interactions of targets. For example, Figure 1 shows several busy scenes which are difficult cases for multi-person tracking.