1 research outputs found
Spatiotemporal KSVD Dictionary Learning for Online Multi-target Tracking
In this paper, we present a new spatial discriminative KSVD dictionary
algorithm (STKSVD) for learning target appearance in online multi-target
tracking. Different from other classification/recognition tasks (e.g. face,
image recognition), learning target's appearance in online multi-target
tracking is impacted by factors such as posture/articulation changes, partial
occlusion by background scene or other targets, background changes (human
detection bounding box covers human parts and part of the scene), etc. However,
we observe that these variations occur gradually relative to spatial and
temporal dynamics. We characterize the spatial and temporal information between
target's samples through a new STKSVD appearance learning algorithm to better
discriminate sparse code, linear classifier parameters and minimize
reconstruction error in a single optimization system. Our appearance learning
algorithm and tracking framework employ two different methods of calculating
appearance similarity score in each stage of a two-stage association: a linear
classifier in the first stage, and minimum residual errors in the second stage.
The results tested using 2DMOT2015 dataset and its public Aggregated Channel
features (ACF) human detection for all comparisons show that our method
outperforms the existing related learning methods.Comment: To appear in Proceedings of 15th Conference on Computer and Robot
Vision 2018 (Oral