School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.
IEEE Trans Image Process. 2013 Jan;22(1):314-25. doi: 10.1109/TIP.2012.2202677. Epub 2012 Jun 5.
Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking algorithm with sparse prototypes, which exploits both classic principal component analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance models. We introduce l(1) regularization into the PCA reconstruction, and develop a novel algorithm to represent an object by sparse prototypes that account explicitly for data and noise. For tracking, objects are represented by the sparse prototypes learned online with update. In order to reduce tracking drift, we present a method that takes occlusion and motion blur into account rather than simply includes image observations for model update. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.
在线目标跟踪是一个具有挑战性的问题，因为它需要学习一个有效的模型来解释由于内在和外在因素引起的外观变化。在本文中，我们提出了一种新颖的基于稀疏原型的在线目标跟踪算法，该算法利用经典的主成分分析（PCA）算法和最近的稀疏表示方案来学习有效的外观模型。我们将 l(1)正则化引入到 PCA 重建中，并开发了一种新的算法，通过稀疏原型来表示物体，明确地考虑了数据和噪声。对于跟踪，使用在线学习的稀疏原型来表示物体并进行更新。为了减少跟踪漂移，我们提出了一种方法，该方法考虑了遮挡和运动模糊，而不仅仅是简单地包含图像观测值来进行模型更新。在具有挑战性的图像序列上的定性和定量评估表明，所提出的跟踪算法在性能上优于几种最先进的方法。
