  In this paper, we focus on the two key aspects of multiple target tracking
problem: 1) designing an accurate affinity measure to associate detections and
2) implementing an efficient and accurate (near) online multiple target
tracking algorithm. As the first contribution, we introduce a novel Aggregated
Local Flow Descriptor (ALFD) that encodes the relative motion pattern between a
pair of temporally distant detections using long term interest point
trajectories (IPTs). Leveraging on the IPTs, the ALFD provides a robust
affinity measure for estimating the likelihood of matching detections
regardless of the application scenarios. As another contribution, we present a
Near-Online Multi-target Tracking (NOMT) algorithm. The tracking problem is
formulated as a data-association between targets and detections in a temporal
window, 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 regularization
into the model. Our ablative analysis verifies the superiority of the ALFD
metric over the other conventional affinity metrics. We run a comprehensive
experimental evaluation on two challenging tracking datasets, KITTI and MOT
datasets. The NOMT method combined with ALFD metric achieves the best accuracy
in both datasets with significant margins (about 10% higher MOTA) over the
state-of-the-arts.
