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

TL;DR

This paper introduces SPAAM, an iterative vehicle tracking method in WAMI that progressively increases temporal context while managing computational complexity through novel pruning and stochastic dis-association techniques.

Contribution

SPAAM is a novel iterative approach that enlarges the temporal window for vehicle association in WAMI by using pruning guided by road networks and stochastic dis-associations to control hypothesis growth.

Findings

Significant performance improvements over three alternative methods.

Effective handling of occlusions and spurious detections.

Maintains computational feasibility with larger temporal windows.

Abstract

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 trade-off 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 SPAAM 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 datasets indicate that our proposed approach provides significant performance improvements over 3 alternatives.