TY - GEN
T1 - Binocular video object tracking with fast disparity estimation
AU - Ye, Yun
AU - Ci, Song
AU - Liu, Yanwei
AU - Wang, Haohong
AU - Katsaggelos, Aggelos K.
PY - 2013
Y1 - 2013
N2 - This paper presents a binocular PTU (pan-tilt unit) camera video object tracking scheme using the MeanShift algorithm and the runtime disparity estimation. The proposed method is to accommodate the requirement of 3D content generation and accurate tracking in more advanced video surveillance applications. The disparity estimation process for each stereoscopic pair is formulated as an energy minimization problem. The iterative solution procedure is implemented in a course-to-fine manner. The estimated disparity is used to scale the tracking window by the MeanShift algorithm, i.e. the size of the tracking area is adjustable according to its inner disparity, and thus the moving object can be better located by the camera. The program maintains the semi-real-time performance and acceptable accuracy as evaluated on a set of standard test data. In our experiment, two PointGrey cameras are controlled through a PTU device. The disparity estimation process on the recorded tracking video (640×480) achieves 6fps on an ordinary PC (2.66GHz CPU, 4GB RAM).
AB - This paper presents a binocular PTU (pan-tilt unit) camera video object tracking scheme using the MeanShift algorithm and the runtime disparity estimation. The proposed method is to accommodate the requirement of 3D content generation and accurate tracking in more advanced video surveillance applications. The disparity estimation process for each stereoscopic pair is formulated as an energy minimization problem. The iterative solution procedure is implemented in a course-to-fine manner. The estimated disparity is used to scale the tracking window by the MeanShift algorithm, i.e. the size of the tracking area is adjustable according to its inner disparity, and thus the moving object can be better located by the camera. The program maintains the semi-real-time performance and acceptable accuracy as evaluated on a set of standard test data. In our experiment, two PointGrey cameras are controlled through a PTU device. The disparity estimation process on the recorded tracking video (640×480) achieves 6fps on an ordinary PC (2.66GHz CPU, 4GB RAM).
UR - http://www.scopus.com/inward/record.url?scp=84890884921&partnerID=8YFLogxK
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U2 - 10.1109/AVSS.2013.6636637
DO - 10.1109/AVSS.2013.6636637
M3 - Conference contribution
AN - SCOPUS:84890884921
SN - 9781479907038
T3 - 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
SP - 183
EP - 188
BT - 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
PB - IEEE Computer Society
T2 - 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
Y2 - 27 August 2013 through 30 August 2013
ER -