STD: A Stereo Tracking Dataset for Evaluating Binocular Tracking Algorithms
Zheng Zhu1,2; Wei Zou1,2; Qingbin Wang1,2; Feng Zhang1,2
2016
会议名称IEEE Conference on Robotics and Biomimetics
会议日期December 3-7, 2016
会议地点Qingdao, China
摘要In this paper, a Stereo Tracking Dataset is proposed for evaluating binocular tracking algorithms. The dataset contains stereoscopic videos which are collected by our mobile platform in different scenarios and videos that are available publicly. All sequences are carefully synchronized and rectified, and the ground truth of object is annotated by authors. Both raw and processed sequences are provided in the dataset. We also develop a Scalable and Occlusion-aware Multi-cues Correlation Filter Tracker (SOMCFT) and evaluate it on the STD. The SOMCFT framework fuses different clues in confidence map level and uses depth information to handle scale changes and occlusion. Quantitative evaluation on STD demonstrates effectiveness of the proposed dataset. All data, including stereo image pairs, calibrations, annotations and attributes, are available for research purposes and comparative evaluation on https://github.com/zhengzhugithub/StereoTracking.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19779
专题精密感知与控制研究中心_精密感知与控制
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Zheng Zhu,Wei Zou,Qingbin Wang,et al. STD: A Stereo Tracking Dataset for Evaluating Binocular Tracking Algorithms[C],2016.
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