Online multi-object tracking via labeled random finite set with appearance learning
MetadataShow full item record
© 2017 IEEE. In this paper, a novel approach to online multi-object tracking is proposed via Labeled Random Finite Sets (RFS) combined with appearance learning. The Labeled RFS formulation of the multi-object state naturally accommodates a time-varying number of objects, track labels, and false positive rejection in a single Bayesian framework. The proposed algorithm exploits appearance feature information for the purpose of learning an object's appearance model, and uses this additional information in the construction an augmented likelihood which improves performance and facilitates track re-initialization. This approach enhances the baseline tracking algorithm and shows better performance with respect to mis-detections, occlusions and false track rejection. Competitive tracking results are shown compared to state-of-the-art algorithms on PETS benchmark  video datasets.
Showing items related by title, author, creator and subject.
Yoon, J.; Yoon, K.; Kim, Du Yong (2013)In this paper, we propose a novel multi-object tracking method to track unknown number of objects with a single camera system. We design the tracking method via probability hypothesis density (PHD) filtering which considers ...
Nguyen, Hoa ; Rezatofighi, H.; Vo, Ba-Ngu ; Ranasinghe, D.C. (2021)We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, ...
Beard, Michael ; Vo, Ba Tuong ; Vo, Ba-Ngu (2020)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, ...