A Multiple Targets Appearance Tracker Based on Object Interaction Models

Abstract

Kernel-based method has been proved to be effective in solving single-target tracking problem. However, facing more complicated multitarget tracking task, most classic kernel-based multitarget trackers do not model the interaction among targets successfully and simply track each target independently. Thus, they usually could not deal with “singularity” problem and fail in tracking the target when occlusions occur or distracters appear. Although multikernel methods may improve the performance by introducing more constraints, how to simulate the relationship and interaction among the tracked targets is still not fully investigated. In this paper, we discuss a very common scenario of multitarget tracking, in which a moving object's motion is not only determined by its virtual destination but also impacted by other neighboring objects. This phenomenon exists in many usual tracking applications such as human tracking, traffic monitoring, video surveillance, and so on, where an object usually moves toward a particular direction but meanwhile detours when close to others to avoid collision. Specifically, we propose a novel interaction model to explain the above phenomenon. Then by defining a new cost function, we embed this interaction model into a kernel-based tracker and further derive our interactive kernel-based multitarget tracker. Experimental results on various datasets demonstrate that our interaction model can alleviate “singularity” problem and, thus, the proposed tracking method could achieve superior performance in multitarget tracking.