Recognition

Complete and accurate video tracking is very difficult to achieve in practice due to occlusions, traffic, shadows and appearance changes. In this paper, we study the feasibility of event recognition when object tracks are fragmented at various levels. By changing the lock score threshold controlling track termination, different levels of track fragmentation are generated. In addition, the data contains lengthy occlusions of objects involved in the events. The effect on event recognition is revealed by examining the event model match score as a function of lock score threshold. Using a Dynamic Bayes network to model events, it is shown that event recognition improves with greater track fragmentation, assuming fragmented tracks for the same object are linked together. This is counter-intuitive, as it implies that event recognition works better as tracking degrades. The experiments also show that the model is capable of handling long tracking gaps. The study is conducted on busy scenes of airplane servicing activities, with occlusions lasting hundreds of frames, object appearance changes and significant clutter traffic.

We present a novel approach to improving moving object detection algorithms for aerial video by incorporating scene understanding. We argue that scene understanding is a valuable input feature for moving object segmentation, and show how it can be used to reduce the false positive rate of any moving object detection algorithm. The approach is especially effective on sequences with large scene depth and much parallax, as often occurs with low, fast-moving sensors. In one difficult sequence, our results show an 8-fold reduction of false positives on average, with essentially no impact on the true positive rate, even when scene understanding is only moderately accurate. We show how scene understanding can be used to increase the accuracy of frame-to-frame homography estimates, and also present a novel approach that uses motion information from the previous frame for outlier rejection at the current frame to further increase accuracy.