Abstract
This paper presents a method for trajectory extraction in videos acquired with a slightly moving camera. Trajectories are initialized at Shi-Tomasi [1] feature points and tracked thanks to the Lucas-Kanade [2] algorithm from the openCV library [3]. New feature points are regularly introduced to compensate for track losses and to handle newly appeared objects. A simple and fast method for camera motion compensation has been implemented, using the fact that near static scene points undergo an equal translation between any two images. Local histograms of displacement normally exhibit a clear peak since our application considers scenes with relatively few moving targets. These peaks designate which tracks and thus which points are best to estimate the homographies representing motion between frames of the sequence. Tracking results for pedestrian and vehicles with camera motion compensation are shown and discussed for two test cases with different environment, scenario and different video quality. The usefulness of camera motion compensated trajectories is demonstrated by an example of target classification based on track maximal speed and possible hotspot detection from long track pauses.
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