An Occlusion Reasoning Scheme for Monocular Pedestrian Tracking in Dynamic Scenes
This addresses pedestrian tracking in dynamic scenes for applications like surveillance, but it is incremental as it builds on existing detection and matching techniques.
The paper tackles pedestrian tracking with a monocular moving camera by proposing an occlusion reasoning scheme that resolves associations between consecutive frames using an affinity matrix and binary integer programming, with verification via SURF matching, and demonstrates efficacy on standard datasets.
This paper looks into the problem of pedestrian tracking using a monocular, potentially moving, uncalibrated camera. The pedestrians are located in each frame using a standard human detector, which are then tracked in subsequent frames. This is a challenging problem as one has to deal with complex situations like changing background, partial or full occlusion and camera motion. In order to carry out successful tracking, it is necessary to resolve associations between the detected windows in the current frame with those obtained from the previous frame. Compared to methods that use temporal windows incorporating past as well as future information, we attempt to make decision on a frame-by-frame basis. An occlusion reasoning scheme is proposed to resolve the association problem between a pair of consecutive frames by using an affinity matrix that defines the closeness between a pair of windows and then, uses a binary integer programming to obtain unique association between them. A second stage of verification based on SURF matching is used to deal with those cases where the above optimization scheme might yield wrong associations. The efficacy of the approach is demonstrated through experiments on several standard pedestrian datasets.