Efficient combination graph model based on conditional random field for online multi-object tracking

Abstract

The joint detection and re-identification (re-ID) strategy shares network features of detection and re-ID, sacrifices the complex probability graph model pairing strategy, and consolidates a two-stage video tracking process into a one-stage, making the multi-object tracking process simple, fast, and accurate. In dense scenes, identified transfer is a major challenge for joint detection and re-ID. To this end, a probability graph model suitable for joint detection and re-ID is presented. The proposed model abandons the idea of matching candidate detections with historical detections in a classical probability graph, uses a scheme to calculate the degree of matching between candidate detections and historical trajectories, and transforms task of ID matching in re-ID process into an energy minimization problem of a conditional random field (CRF). However, the solution space of general CRF is complex and requires an iterative search. To achieve efficient online tracking, the original CRF problem is approximately transformed into a combination of multiple CRF problems with closed-form solutions. Moreover, the proposed algorithm has been applied in practical applications using an edge-cloud model that maintains the balance between performance and efficiency. Extensive experiments on the well-known MOTchallenge benchmark demonstrate the superior performance of the proposed algorithm.