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An Efficient Edge Artificial Intelligence Multi-pedestrian Tracking Method with Rank Constraint


Class Agnostic Image Common Ob
Abstract


Characterized by the ability to handle varying number of objects, tracking by detection framework becomes increasingly popular in multi-object tracking (MOT) problem. However, the tracking performance heavily depends on the object detector. Considering data association optimization and association affinity model are two key parts in MOT, an online multi-pedestrian tracking method is proposed to formulate a more effective association affinity model. It includes a two-step data association taking advantage of rank based dynamic motion affinity model. The rank based dynamic motion affinity model is used to estimate the object state and refine the trajectory for each of target to achieve the noiseless trajectory. Both strategies are beneficial to eliminate ambiguous detection responses during association. To fairly verify the proposed method, three public datasets are adopted. Both qualitative and quantitative experiment results demonstrate the superiorities of the proposed tracking algorithm in comparison with its counterparts.

KeyWords
Multi-object tracking, Rank-based dynamic tracklet, Edge artificial intelligence



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