STD: A Stereo Tracking Dataset for Evaluating Binocular Tracking Algorithms

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 精密感知与控制

	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	中科院工业视觉智能装备工程实验室_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
第一作者单位	中国科学院自动化研究所
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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文件名称/大小	文献类型	版本类型	开放类型	使用许可
STD_A Stereo Trackin（739KB）	会议论文		开放获取	CC BY-NC-SA	浏览下载