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In this paper, we focus on the two key aspects of multiple target trackingproblem: 1) designing an accurate affinity measure to associate detections and2) implementing an efficient and accurate (near) online multiple targettracking algorithm. As the first contribution, we introduce a novel AggregatedLocal Flow Descriptor (ALFD) that encodes the relative motion pattern between apair of temporally distant detections using long term interest pointtrajectories (IPTs). Leveraging on the IPTs, the ALFD provides a robustaffinity measure for estimating the likelihood of matching detectionsregardless of the application scenarios. As another contribution, we present aNear-Online Multi-target Tracking (NOMT) algorithm. The tracking problem isformulated as a data-association between targets and detections in a temporalwindow, that is performed repeatedly at every frame. While being efficient,NOMT achieves robustness via integrating multiple cues including ALFD metric,target dynamics, appearance similarity, and long term trajectory regularizationinto the model. Our ablative analysis verifies the superiority of the ALFDmetric over the other conventional affinity metrics. We run a comprehensiveexperimental evaluation on two challenging tracking datasets, KITTI and MOTdatasets. The NOMT method combined with ALFD metric achieves the best accuracyin both datasets with significant margins (about 10% higher MOTA) over thestate-of-the-arts.

Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor