The focus of this paper is a novel object tracking algorithm which combines an incrementally updated subspace-based appearance model, reconstruction error likelihood function and a two stage selective sampling importance resampling particle filter with motion estimation through autoregressive filtering techniques. The primary contribution of this paper is the use of multiple bags of subspaces with which we aim to tackle the issue of appearance model update. The use of a multibag approach allows our algorithm to revert to a previously successful appearance model in the event that the primary model fails. The aim of this is to eliminate tracker drift by undoing updates to the model that lead to error accumulation and to redetect targets after periods of occlusion by removing the subspace updates carried out during the period of occlusion. We compare our algorithm with several state-of-the-art methods and test on a range of challenging, publicly available image sequences. Our findings indicate a significant robustness to drift and occlusion as a result of our multibag approach and results show that our algorithm competes well with current state-of-the-art algorithms.
- particle filter tracking
- multibag approach
- appearance model
- object tracking
- particle filter
- selective sampling importance resampling
Jenkins, M. D., Barrie, P., Buggy, T., & Morison, G. (2018). Selective sampling importance resampling particle filter tracking with multibag subspace restoration. IEEE Transactions on Cybernetics, 48(1), 264-276. https://doi.org/10.1109/TCYB.2016.2631660