Understanding and Diagnosing Visual Tracking Systems

王乃岩港科

欢迎关注我们最新的工作"Understanding and Diagnosing Visual Tracking Systems"，我们深度剖析了现有追踪系统中每个组成部分对结果的影响，得出了一些很有意思的结论。我们只使用HOG+logistic regression就得到了和目前最好的tracker持平的结果

http://arxiv.org/abs/1504.06055


Understanding and Diagnosing Visual Tracking SystemsNaiyan Wang, Jianping Shi, Dit-Yan Yeung, Jiaya Jia
(Submitted on 23 Apr 2015)

Several benchmark datasets for visual tracking research have been proposed in recent years. Despite their usefulness, whether they are sufficient for understanding and diagnosing the strengths and weaknesses of different trackers remains questionable. To address this issue, we propose a framework by breaking a tracker down into five constituent parts, namely, motion model, feature extractor, observation model, model updater, and ensemble post-processor. We then conduct ablative experiments on each component to study how it affects the overall result. Surprisingly, our findings are discrepant with some common beliefs in the visual tracking research community. We find that the feature extractor plays the most important role in a tracker. On the other hand, although the observation model is the focus of many studies, we find that it often brings no significant improvement. Moreover, the motion model and model updater contain many details that could affect the result. Also, the ensemble post-processor can improve the result substantially when the constituent trackers have high diversity. Based on our findings, we put together some very elementary building blocks to give a basic tracker which is competitive in performance to the state-of-the-art trackers. We believe our framework can provide a solid baseline when conducting controlled experiments for visual tracking research.