Tracking and detection of moving objects in long motion sequence

Most work on feature tracking tend to solve the problem in a small number of frames. Such approaches require assumptions on the nature of objects and, more importantly, lead to complex and unstable algorithms. By using long image sequence with high temporal sampling rate, we formulate the problem using regularization approach such that the class of admissible trajectories is restricted by imposing constraints on the smoothness of motion. The solution is obtained by the method of energy minimization. The efficacy of this approach is demonstrated with extensive empirical studies on real and synthetic motion sequences. In addition, we deal with the issue of automatic feature extraction explicitly. A unified framework for parallel extracting various kind of features including edge, corner and surface is developed. Furthermore, this framework is implemented on the Intel Paragon which yields substantial speedup with more than one hundred processors. Finally, the problem of moving object detection in long sequence where the camera is moving in a straight path is considered. In our algorithm, a set of 2-D temporal slices is extracted from the sequence based on the tracking result. The problem of moving object detection is converted to the inspection of gradient direction on the temporal slices. Moreover, the algorithm is capable of detecting multiple moving objects in the scene