Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow

Asad A. Butt, Robert T. Collins
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
We propose a method for global multi-target tracking that can incorporate higher-order track smoothness constraints such as constant velocity. Our problem formulation readily lends itself to path estimation in a trellis graph, but unlike previous methods, each node in our network represents a candidate pair of matching observations between consecutive frames. Extra constraints on binary flow variables in the graph result in a problem that can no longer be solved by min-cost network flow. We
more » ... efore propose an iterative solution method that relaxes these extra constraints using Lagrangian relaxation, resulting in a series of problems that ARE solvable by min-cost flow, and that progressively improve towards a high-quality solution to our original optimization problem. We present experimental results showing that our method outperforms the standard network-flow formulation as well as other recent algorithms that attempt to incorporate higher-order smoothness constraints.
doi:10.1109/cvpr.2013.241 dblp:conf/cvpr/ButtC13 fatcat:wjgnjrmsjfhgddpznaa2ohl46a