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Multi-Object Tracking (MOT) is a popular topic in computer vision. However,identity issue, i.e., an object is wrongly associated with another object of adifferent identity, still remains to be a challenging problem. To address it,switchers, i.e., confusing targets thatmay cause identity issues, should befocused. Based on this motivation,this paper proposes a novel switcher-awareframework for multi-object tracking, which consists of Spatial Conflict Graphmodel (SCG) and Switcher-Aware Association (SAA). The SCG eliminates spatialswitch-ers within one frame by building a conflict graph and working out theoptimal subgraph. The SAA utilizes additional information from potentialtemporal switcher across frames, enabling more accurate data association.Besides, we propose a new MOT evaluation measure, Still Another IDF score(SAIDF), aiming to focus more on identity issues.This new measure may overcomesome problems of the previous measures and provide a better insight foridentity issues in MOT. Finally,the proposed framework is tested under both thetraditional measures and the new measure we proposed. Extensive experimentsshow that ourmethod achieves competitive results on all measure.

SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure