SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation

Xiao Wang, Chenglong Li, Bin Luo, Jin Tang

School of Computer Science and Technology, Anhui University, Hefei, China

Abstruct:

Existing visual trackers are easily disturbed by occlusion, blur and large deformation. We think the performance of existing visual trackers may be limited due to the following issues: i) Adopting the dense sampling strategy to generate positive examples will make them less diverse; ii) The training data with different challenging factors are limited, even through collecting large training dataset. Collecting even larger training dataset is the most intuitive paradigm, but it may still can not cover all situations and the positive samples are still monotonous. In this paper, we propose to generate hard positive samples via adversarial learning for visual tracking. Specifically speaking, we assume the target objects all lie on a manifold, hence, we introduce the positive samples generation network (PSGN) to sampling massive diverse training data through traversing over the constructed target object manifold. The generated diverse target object images can enrich the training dataset and enhance the robustness of visual trackers. To make the tracker more robust to occlusion, we adopt the hard positive transformation network (HPTN) which can generate hard samples for tracking algorithm to recognize. We train this network with deep reinforcement learning to automatically occlude the target object with a negative patch. Based on the generated hard positive samples, we train a Siamese network for visual tracking and our experiments validate the effectiveness of the introduced algorithm.

Network Architecture:

Experimental Results:

Further Extensions

1. Integrate with MDNet to validate the generic ;

2. Introduce natural language descriptions to boost the feature learning of CNN ;

【Note】 Still in progressing, related results will coming soon.



If you find it useful for your research, please consider to cite the following paper:

@InProceedings{Wang_2018_CVPR,

author = {Wang, Xiao and Li, Chenglong and Luo, Bin and Tang, Jin},

title = {SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

month = {June},

year = {2018}

}