Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking

This paper proposes a novel formulation for the multi-object tracking-by-detection paradigm for two (or more) input detectors. Using full-body and heads detections, the fusion helps to recover heavily occluded persons and to reduce false positives. The assignment of the two input features to a person and the extraction of the trajectories is commonly solved from one binary quadratic program (BQP). Due to the computational complexity of the NP-hard QP, we approximate the solution using the Frank-Wolfe algorithm. We propose several improvements to this solver affecting better minimization and shorter computations, compared to off-the-shelf BQP-solvers and the standard Frank-Wolfe algorithm. Evaluation on pedestrian tracking is provided for multiple scenarios, showing improved tracking quality over single input feature trackers and standard QP-solvers. Finally we present the performance of our tracker on the challenging \MOTNEW benchmark, being comparable to state-of-the-art trackers.