Yang Honghong, Qu Shiru
Department of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Comput Intell Neurosci. 2016;2016:5894639. doi: 10.1155/2016/5894639. Epub 2016 Aug 18.
Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.
近年来，基于稀疏表示的目标跟踪取得了令人瞩目的跟踪结果。然而，稀疏表示框架下的跟踪器总是过度强调稀疏表示，而忽略了视觉信息的相关性。此外，稀疏编码方法仅独立地对局部区域进行编码，而忽略了图像的空间邻域信息。在本文中，我们提出了一种鲁棒的跟踪算法。首先，使用多个互补特征来描述目标外观；通过瞬时和稳定的外观特征同时对跟踪目标的外观模型进行建模。采用一种考虑图像块空间邻域信息和计算负担的两阶段稀疏编码方法来计算重建的目标外观。然后，通过瞬态和重建外观模型的跟踪似然函数来衡量每个跟踪器的可靠性。最后，通过一个成熟的粒子滤波器框架获得最可靠的跟踪器；基于当前跟踪结果对训练集和模板库进行增量更新。在不同具有挑战性的视频序列上的实验结果表明，所提出的算法具有良好的跟踪精度和鲁棒性。
