Online Multi-Object Tracking Using Hierarchical Constraints for Complex Scenarios

Online multi-object tracking (MOT) in an intelligent vehicle platform aims at locating the surrounding objects in real time, which remains far from being solved in complex scenarios, due to various motion patterns of tracked objects and severe occlusions caused by cluttered background or other objects. In this paper, we establish a unified online MOT framework for complex scenarios that employs a hierarchical model to improve the solution of data association, termed hierarchical MOT (HMOT). Incorporating the multiple Gaussians uncertainty theory into the individual motion model for each target followed by imposing interaction constraint to re-associate the tracklets with lower confidence leads our algorithm to achieve accurate multi-object tracking. With such a model, individual objects are not only more precisely associated across frames, but also dynamically constrained with each other in a global manner. Experiments on challenging data sets verify the performance of the proposed HMOT approach over the other state-of-the-art MOT methods.

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