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| Object tracking based on particle filter and online random forest classification |
| 1.College of Information Engineering, Xiangtan University, Xiangtan, Hunan 411105, China; 2.Key Laboratory of Intelligent Computing ＆ Information Processing, Ministry of Education, Xiangtan University, Xiangtan, Hunan 411105, China; 3.School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China; 4.Department of Computer Science and Technology, Hu-aihua University, Huaihua, Hunan 418008, China |
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Abstract To avoid the performance degradation of the tracker caused by the inaccurate prediction of state model and inaccurate observation in particle filter, a new framework was proposed based on particle filter and online random forest for object tracking. The probability density of the object appearance was accurately approximated by the sample set which was collected by online learning method. In particle filter scheme, the particle likelihood was measured by the combination of classified result of online random forest and region histogram likelihood to improve the accuracy of the observation model. While track drift occurred, the particle filter was re-initialized by the detection of random forest to prevent track failure caused by the accumulated error. The algorithm was realized by vc 6.0+ opencv, and two experiments were designed to verify the tracking precision and the ability of drift resistance. The results show that the proposed algorithm can increase the ratio of correct tracking to 91%, while those are 68% and 75% for the particle filter and the random forest, respectively. The proposed approach can prevent track drift to achieve robust long sequences tracking.
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Received: 26 October 2012
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2014, Vol. 35 
