Vehicle Tracking in Wide Area Motion Imagery via Stochastic Progressive Association Across Multiple Frames

Vehicle tracking in wide area motion imagery (WAMI) relies on associating vehicle detections across multiple WAMI frames to form tracks corresponding to individual vehicles. The temporal window length, i.e., the number M of sequential frames, over which associations are collectively estimated poses a tradeoff between accuracy and computational complexity. A larger M improves performance because the increased temporal context enables the use of motion models and allows occlusions and spurious detections to be handled better. The number of total hypotheses tracks, on the other hand, grows exponentially with increasing M, making larger values of M computationally challenging to tackle. In this paper, we introduce stochastic progressive association across multiple frames, an iterative approach that progressively grows M with each iteration to improve estimated tracks by exploiting the enlarged temporal context while keeping computation manageable through two novel approaches for pruning association hypotheses. First, guided by a road network, accurately co-registered to the WAMI frames, we disregard unlikely associations that do not agree with the road network. Second, as M is progressively enlarged at each iteration, the related increase in association hypotheses is limited by revisiting only the subset of association possibilities rendered open by stochastically determined dis-associations for the previous iteration. The stochastic dis-association at each iteration maintains each estimated association according to an estimated probability for confidence, obtained via a probabilistic model. Associations at each iteration are then estimated globally over the M frames by (approximately) solving a binary integer programming problem for selecting a set of compatible tracks. Vehicle tracking results obtained over test WAMI data sets indicate that our proposed approach provides significant performance improvements over the state-of-the-art alternatives.