UMD_VDT, an Integration of Detection and Tracking Methods for Multiple Human Tracking

We integrate human detection and regional affine invariant feature tracking into a robust human tracking system. First, foreground blobs are detected using background subtraction. The background model is built with a local predictive model to cope with large illumination changes. Detected foreground blobs are then used by a box tracker to establish stable tracks of moving objects. Human detection hypotheses are detected using a combination of both shape and region information through a hierarchical part-template matching method. Human detection results are then used to refine tracks for moving people. Track refinement, extension and merging are carried out with a robust tracker that is based on regional affine invariant features. We show experimental results for the separate components as well as the entire system. 1 Overview of UMD VDT Most human activity analysis and understanding approaches in visual surveillance take human tracks as their input. Establishing accurate human tracks is therefore very important in many visual surveillance systems. Even though it has been long studied, accurate human tracking is still a challenge due to a number of reasons such as shape and pose variation, occlusion and object grouping. We describe a multiple object tracking system that is an integration of human detection and general object tracking approaches. Our system detects people automatically using a probabilistic human detector and tracks them as they move through the scene. It is able to resolve partial occlusion and merging, especially when people walk in groups. Figure 1 shows the diagrammatic overview of our system. It consists of three main components: background subtraction, human detection and human tracking. Their details will be discussed in the next sections (Section 2, 3, and 4). In this section, we describe the overall procedure that is built on top of these components. 1.1 Algorithm Integration Foreground Detection First, background subtraction (section 2) is applied to detect foreground blobs. To improve the detection rate, we combine background subtraction with a shortterm frame difference. The background subtraction module is designed to cope