Online multi-object tracking by quadratic pseudo-boolean optimization

Publication Type:
Conference Proceeding
IJCAI International Joint Conference on Artificial Intelligence, 2016, 2016-January pp. 3396 - 3402
Issue Date:
Filename Description Size
Online.pdfPublished version3.14 MB
Adobe PDF
Full metadata record
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.
Please use this identifier to cite or link to this item: