Robust Computation of Optical Flow in a Multi-Scale Differential Framework

We have developed a new algorithm for computing optical flow in a differential framework. The image sequence is first convolved with a set of linear, separable spatiotemporal filters similar to those that have been used in other early vision problems such as texture and stereopsis. Our analysis of the measurement errors leads us to develop an algorithm based on a robust version of total least squares. Each optical flow vector computed has an associated reliability measure which can be used in subsequent processing. The performance of the algorithm on the data set used by Barron et al. (CVPR 1992) compares favorably with other techniques. In addition to being separable, the filters used are also causal, incorporating only past time frames. The algorithm is fully parallel and has been implemented on a multiple processor machine. By being fully parallel, the algorithm can be performed by an array of processors in real time. In addition, the differential method is computationally less expensive than matching methods for computing visual motion. The output of the linear filters can also be used in other visual tasks such as stereo and recognition. Thus, this approach to motion detection can be part of a real time vision application system in which linear filters provide a basis for visual tasks such as passive ranging and moving object detection. For vehicle surveillance, the system provides individual vehicle speeds and directions. For autonomous vehicles, the system would provide both stereo correspondence for range information andoptical flow for collision avoidance in a single computational framework.