  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.
