ADAPTIVE AGGREGATED TRACKLET LINKING FOR MULTI-FACE TRACKING

This paper addresses the problem of multi-face tracking. The proposed framework first assembles discrete detections into short reliable sequences (code-named ‘tracklets’) and second, links them via hierarchical clustering. A key challenge in face tracking in real world scenarios ‘on the wild’ or under adverse conditions is the drastic variations across scenes (e.g: variations in illumination, pose, occlusion, motion blur, etc.). To this end, the paper presents a simple hierarchical clustering approach which exploits the intra-scene compactness while minimizing the impact of inter-scene variations. The algorithm adapts to different domains by dynamically learning domain-specific parameters through density dispersion analysis. The proposed framework is evaluated on three databases, which demonstrate competitive performance with state-of the-art clustering and tracklet linking approaches. In addition, the paper presents a challenging database which is publicly available, on which the end-to-end tracking performance is reported.