Local Track Repair for Video Tracking on Small UAVs

摘要

Persistent aerial video surveillance from small UAV (SUAV) platforms requires accurate and robust target tracking capabilities. However, video tracks can break due to excessive camera motion, target resolution, low signal-to noise ratio, video frame dropout, and frame-to-frame registration errors. Connecting broken tracks (video track repair) is thus essential for maintaining high quality target tracks. In this paper we present an approach to track repair based on multi-hypothesis sequential probability ratio tests (MHSPRT) that is suitable for real-time video tracking applications. To reduce computational complexity, the approach uses a target dynamics model whose state estimation covariance matrix has an analytic eigendecomposition. Chi-square gating is used to form feasible track-to-track associations, and a set of local hypothesis tests is defined for associating new tracks with coasted tracks. Evidence is accumulated across video frames by propagating posterior probabilities associated with each track repair hypothesis in the MHSPRT framework. Global maximum likelihood and maximum a posteriori estimation techniques resolve conflicts between local track association hypotheses. The approach also supports fusion of appearance-based features to augment statistical distributions of the track state and enhance performance during periods of kinematic ambiguity. First, an overview of the video tracker technology is presented. Next the track repair algorithm is described. Finally, numerical results are reported demonstrating performance on real video data acquired from an SUAV.