Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Department of Electronics Engineering, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel). 2014 Feb 17;14(2):3130-55. doi: 10.3390/s140203130.
To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.
针对基于视频传感器的应用的鲁棒目标跟踪问题，提出了一种基于增量判别结构字典学习（IDSDL-VT）的在线判别算法。在我们的框架中，设计了一个判别字典，它结合了正、负和琐碎的补丁，以稀疏表示重叠的目标补丁。然后，提出了一种局部更新（LU）策略用于稀疏系数学习。为了描述训练和分类过程，提出了一种基于 K 个组合投票（KCV）函数的多线性分类器组。随着字典的不断发展，模型也被训练以实时适应目标外观的变化。与最先进算法的挑战性图像序列的定性和定量评估表明，所提出的跟踪算法具有更好的性能。我们还说明了它在视觉传感器网络中的中继应用。
