Wang Xin, Shen Siqiu, Ning Chen, Zhang Yuzhen, Lv Guofang
J Opt Soc Am A Opt Image Sci Vis. 2017 Apr 1;34(4):533-544. doi: 10.1364/JOSAA.34.000533.
Despite much success in the application of sparse representation to object tracking, most of the existing sparse-representation-based tracking methods are still not robust enough for challenges such as pose variations, illumination changes, occlusions, and background distractions. In this paper, we propose a robust object-tracking algorithm via local discriminative sparse representation. The key idea in our method is to develop what we believe is a novel local discriminative sparse representation method for object appearance modeling, which can be helpful to overcome issues such as appearance variations and occlusions. Then a robust tracker based on the local discriminative sparse appearance model is proposed to track the object over time. Additionally, an online dictionary update strategy is introduced in our approach for further robustness. Experimental results on challenging sequences demonstrate the effectiveness and robustness of our proposed method.
尽管在将稀疏表示应用于目标跟踪方面取得了很大成功，但大多数现有的基于稀疏表示的跟踪方法在面对诸如姿态变化、光照变化、遮挡和背景干扰等挑战时，仍然不够鲁棒。在本文中，我们提出了一种基于局部判别稀疏表示的鲁棒目标跟踪算法。我们方法的关键思想是开发一种我们认为新颖的用于目标外观建模的局部判别稀疏表示方法，这有助于克服诸如外观变化和遮挡等问题。然后提出了一种基于局部判别稀疏外观模型的鲁棒跟踪器来随时间跟踪目标。此外，我们的方法还引入了一种在线字典更新策略以进一步提高鲁棒性。在具有挑战性的序列上的实验结果证明了我们提出的方法的有效性和鲁棒性。
