A Solution for Large-Scale Multi-Object Tracking

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Abstract

A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking a very large, unknown and time-varying number of objects simultaneously, in the presence of a high number of false alarms, as well as missed detections and measurement origin uncertainty due to closely spaced objects. The algorithm is demonstrated on a simulated tracking scenario, where the peak number objects appearing simultaneously exceeds one million. Additionally, we introduce a new method of applying the optimal sub-pattern assignment (OSPA) metric to determine a meaningful distance between two sets of tracks. We also develop an efficient strategy for its exact computation in large-scale scenarios to evaluate the performance of the proposed tracker.

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Beard, M., Vo, B. T., & Vo, B. N. (2020). A Solution for Large-Scale Multi-Object Tracking. IEEE Transactions on Signal Processing, 68, 2754–2769. https://doi.org/10.1109/TSP.2020.2986136

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