Multitarget Occlusion Tracking With 3-D Spatio-Temporal Context Graph Model

In complex tracking scenarios of multitarget occlusion, especially when the target is seriously occluded, feature loss can lead to tracking failure. In this article, we develop a multitarget occlusion tracking algorithm based on a 3-D spatio-temporal context graph model, which can fully utilize the targets’ RGB-D information. Specifically, we first extract target’s 3-D features containing appearance, motion, spatial, and temporal features. Then, the spa’Io-temporal context features between different targets are also be acquired. Furthermore, the attribute relational graph (ARG) model with target’s 3-D spatio-temporal context features is constructed, which combines spatial features in the current frame and temporal features between consecutive frames. Moreover, if the target is determined to be occluded according to the matching results in the current frame, the probability relaxation method is used to assign the optimal tracking label to clustering blocks in occlusion area by matching these ARG models, which can address the multitarget cross occlusion problem. Extensive experiments on the Princeton Tracking Benchmark Datasets clearly demonstrate that our method can achieve superior performance against the typical algorithms proposed in recent years.