Vehicle tracking in Wide area motion imagery: A facility location motivated combinatorial approach
We propose a practical combinatorial approach for addressing the challenging task of vehicle tracking in Wide area motion imagery (WAMI) by leveraging a pixel accurate co-registered vector road-map. Specifically, guided by the co-registered road network, we obtain a sparse trellis graph linking each vehicle detection (VD) in a WAMI frame with a road reachable VD in the next frame, which then allows us to enumerate all possible hypotheses tracks. The globally optimal selection of tracks over a K frame window is then formulated as a minimum cost K capacitated facility location problem, where each hypothesis track is allocated VDs in each frame and assigned a cost that models desirable properties of vehicle track. Computation of optimized combinations of jointly feasible tracks that minimize the total cost for the tracks becomes feasible in our formulation for moderate values of K by utilizing available solvers for the facility location problem. The approach automatically selects the optimal number of tracks and provides flexibility in defining costs for tracks globally across the K frames. Vehicle tracking results obtained over test WAMI datasets indicate that our proposed method provides significant better performance than two other state of the art alternatives.