TY - GEN
T1 - Tracking of multiple objects under partial occlusion
AU - Han, Bing
AU - Paulson, Christopher
AU - Lu, Taoran
AU - Wu, Dapeng
AU - Li, Jian
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2009
Y1 - 2009
N2 - The goal of multiple object tracking is to find the trajectory of the target objects through a number of frames from an image sequence. Generally, multi-object tracking is a challenging problem due to illumination variation, object occlusion, abrupt object motion and camera motion. In this paper, we propose a multi-object tracking scheme based on a new weighted Kanade-Lucas-Tomasi (KLT) tracker. The original KLT tracking algorithm tracks global feature points instead of a target object, and the features can hardly be tracked through a long sequence because some features may easily get lost after multiple frames. Our tracking method consists of three steps: the first step is to detect moving objects; the second step is to track the features within the moving object mask, where we use a consistency weighted function; and the last step is to identify the trajectory of the object. With an appropriately chosen weighting function, we are able to identify the trajectories of moving objects with high accuracy. In addition, our scheme is able to handle partial object occlusion. © 2009 SPIE.
AB - The goal of multiple object tracking is to find the trajectory of the target objects through a number of frames from an image sequence. Generally, multi-object tracking is a challenging problem due to illumination variation, object occlusion, abrupt object motion and camera motion. In this paper, we propose a multi-object tracking scheme based on a new weighted Kanade-Lucas-Tomasi (KLT) tracker. The original KLT tracking algorithm tracks global feature points instead of a target object, and the features can hardly be tracked through a long sequence because some features may easily get lost after multiple frames. Our tracking method consists of three steps: the first step is to detect moving objects; the second step is to track the features within the moving object mask, where we use a consistency weighted function; and the last step is to identify the trajectory of the object. With an appropriately chosen weighting function, we are able to identify the trajectories of moving objects with high accuracy. In addition, our scheme is able to handle partial object occlusion. © 2009 SPIE.
KW - Consistency weighted function
KW - KLT tracker
KW - Object detection
KW - Object occlusion
KW - Object tracking
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-69649095016&origin=recordpage
U2 - 10.1117/12.814987
DO - 10.1117/12.814987
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9780819476012
VL - 7335
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Automatic Target Recognition XIX
T2 - Automatic Target Recognition XIX
Y2 - 13 April 2009 through 14 April 2009
ER -