In this paper, we propose a discriminative multitask sparse learning scheme for object tracking in a particle filter framework. By representing each particle as a linear combination of adaptive dictionary templates, we utilise the correlations among different particles (tasks) to obtain a better representation and a more efficient scheme than learning each task individually. However, this model is completely generative and the designed tracker may not be robust enough to prevent the drifting problem in the presence of rapid appearance changes. In this paper, we use a Conditional Random Field (CRF) along with the multitask sparse model to extend our scheme to distinguish the object candidate from the background particle candidate. By this way, the number of particle samples is reduced significantly, while we make the tracker more robust. The proposed algorithm is evaluated on 11 challenging sequences and the results confirm the effectiveness of the approach and significantly outperforms the state-of-the-art trackers in terms of accuracy measures including the centre location error and the overlap ratio, respectively.
Bozorgtabar, B., & Goecke, R. (2015). Discriminative multi-task sparse learning for robust visual tracking using conditional random field. In 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/DICTA.2014.7008102