Scene-Adaptive Hierarchical Data Association for Multiple Objects Tracking

Obtaining reliable and discriminative target representation are two vital tasks for data association in multi-tracking. Pervious works always directly combine bunch of features for more discriminative target representation, but this is prone to error accumulation and unnecessary computational cost, which on the contrary may increase identity switches in data association. Moreover, reliability of a same feature in different scenes may vary a lot, especially for currently widespread network cameras, which have been settled in complex and various scenes, previous fixed feature selection scheme cannot meet general requirements. To address this problem, we propose a scene-adaptive hierarchical data association scheme, which adaptively selects features which have higher reliability on target representation in applied scene, and gradually combines features to the minimum requirements of discriminating ambiguous targets. Hierarchical feature space is constructed according to reliability of features in the multi-tracking system, and data association is conducted in different layers of the feature space adaptively. Our algorithm is validated on various challenging RGB-D and RGB datasets recorded in various indoor and outdoor scenes, for diversities of both features and scenes. Experimental results validate its effectiveness and efficiency.