Correlation-Based Tracking of Multiple Targets With Hierarchical Layered Structure
提出一种分层跟踪结构，利用目标间的相关性建立互助机制，通过非线性运动模型和交互模型预测目标区域，并用运动熵衡量场景异质性，提升复杂场景下多目标跟踪性能。
Visual target tracking is one of the most important research areas in the field of computer vision. Within this realm, multiple targets tracking (MTT) under complicated scene stands out for its great availability in real life applications, such as urban traffic surveillance and sports video analysis. However, in MTT, main difficulties arise from large variation in target saliency and significant motion heterogeneity, which may result in the failure of tracking weak targets. To tackle this challenge, a novel hierarchical layered tracking structure is proposed to perform tracking sequentially layer-by-layer. Upon this layered structure, we establish an intertarget mutual assistance mechanism on basis of intertarget correlation exploited among targets. The tracking results of a subset of targets can be utilized as additional prior information for tracking other targets. Specifically, a nonlinear motion model as well as a target interaction model basing on the intertarget correlation are proposed to effectively estimate the possible target region-of-interest to facilitate the prediction-based tracking. Moreover, the concept of motion entropy is introduced to quantitatively measure the degree of motion heterogeneity within the tracking scene for layer construction. Compared to other existing methods, extensive experiments demonstrated that the proposed method is capable of achieving higher tracking performance in complicated scenes, where targets are characterized with great heterogeneity.