关键词: 多域卷积神经网络（MDNet）, 快速多域卷积神经网络（FasterMDNet）, 视频目标跟踪, 区域建议网络（RPN）, 候选区域建议框（ROI）, ROIAlign

Abstract:

Multi-Domain convolutional neural Network（MDNet） algorithm uses selective search in the convolutional layer to extract candidate boxes, therefore, it cannot share full image features, resulting in slow tracking of online video target. A Faster Multi-Domain convolutional neural Network（FasterMDNet） video target tracking algorithm is proposed to solve this problem. FasterMDNet is based on MDNet, it adds the RPN（Region Proposal Network） behind the convolutional layer, optimizes the loss function, shares the full image convolution feature, and accelerates the generation of candidate ROI（Region of Interest） more efficiently. In order to better obtain the target and background information representation, the ROIAlign layer is added after the RPN network, for the feature map of extracted candidate ROI, which uses bilinear interpolation to improve receptive field resolution. The proposed algorithm is evaluated in multiple target tracking benchmark datasets including OTB2013, OTB2015, and VOT2016, and compares with the state-of-the-art algorithm. Experimental results show that tracking performance of the proposed algorithm is better than other comparison methods, and the online tracking speed is nearly 12 times faster than MDNet algorithm in the same experimental environment.

Key words: Multi-Domain convolutional neural Network（MDNet）, Faster Multi-Domain convolutional neural Network（FasterMDNet）, video target tracking, Region Proposal Network（RPN）, Region of Interest（ROI）, ROIAlign