A Solution for Large Scale Multi Object Tracking
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.
IEEE Transactions on Signal Processing