Transcription of High Performance Visual Tracking With Siamese Region ...
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High Performance Visual Tracking With Siamese Region ...
High Performance Visual Tracking with Siamese Region proposal NetworkBo Li1,2, Junjie Yan3,Wei Wu1, Zheng Zhu1,4,5, Xiaolin Hu31 SenseTime Group Limited2 Beihang University3 Tsinghua University4 Institute of Automation, Chinese Academy of Sciences5 University of Chinese Academy of object Tracking has been a fundamental topic inrecent years and many deep learning based trackers haveachieved state-of-the-art Performance on multiple bench-marks. However, most of these trackers can hardly get topperformance with real-time speed. In this paper , we pro-pose the Siamese Region proposal network ( Siamese -RPN)which is end-to-end trained off-line with large-scale imagepairs. Specifically, it consists of Siamese subnetwork forfeature extraction and Region proposal subnetwork includ-ing the classification branch and regression branch. In theinference phase, the proposed framework is formulated as alocal one-shot detection task. We can pre-compute the tem-plate branch of the Siamese subnetwork and formulate thecorrelation layers as trivial convolution layers to performonline Tracking .
In this paper, we show that the off-line trained deep learning based tracker can achieve competitive results com-pared to the state-of-the-art correlation filter based methods when properly designed. The key is the proposed Siamese region proposal network (Siamese-RPN). It consists of a template branch and a detection branch, which are trained
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