Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Online Multi-Object Tracking by Quadratic Pseudo-Boolean Optimization / 3396
Long Lan, Dacheng Tao, Chen Gong, Naiyang Guan, Zhigang Luo

Online multi-object tracking (MOT) is challenging: frame-by-frame matching of detection hypotheses to the correct trackers can be difficult. The Hungarian algorithm is the most commonly used online MOT data association method due to its rapid assignment; however, the Hungarian algorithm simply considers associations based on an affinity model. For crowded scenarios, frequently occurring interactions between objects complicate associations, and affinity-based methods usually fail in these scenarios. Here we introduce quadratic pseudo-Boolean optimization (QPBO) to an online MOT model to analyze frequent interactions. Specifically, we formulate two useful interaction types as pairwise potentials in QPBO, a design that benefits our model by exploiting informative interactions and allowing our online tracker to handle complex scenes. The auxiliary interactions result in a non-submodular QPBO, so we accelerate our online tracker by solving the model with a graph cut combined with a simple heuristic method. This combination achieves a reasonable local optimum and, importantly, implements the tracker efficiently. Extensive experiments on publicly available datasets from both static and moving cameras demonstrate the superiority of our method.