Ensemble Tracking Based on Randomized Trees
Object tracking is an active yet challenging research topic in computer vision. Recently, a trend to treat the problem as a classification problem is boom. By such a paradigm, a discriminative classifier is trained and updated during tracking procedure. In this paper, the ensemble of randomized trees such as random forests or extremely randomized trees is employed to construct a discriminative appearance model to accomplish tracking task. Benefited from the noise insensitivity and operation efficiency of randomized trees, the appearance model used for tracking can be efficiently updated through growing new trees to substitute the degraded ones. Meanwhile, mean shift is introduced to locate the object in each newly arrived frame. Extensive experiments are performed to compare the proposed algorithm with four wellknown tracking algorithms on several challenging video sequences. Convincing results demonstrate that the proposed tracker manages to handle illumination changes and pose variations.
Visual tracking random forests extremely randomized trees adaptive appearance model
GU Xingfang MAO Yaobin KONG Jianshou
School of Automation, Nanjing University of Science and Technology, Nanjing, China 210094
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3818-3823
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）