30 October 2009 SVM based classification of moving objects in video
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Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 749607 (2009) https://doi.org/10.1117/12.832622
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
In this paper, a classification method of four moving objects including vehicle, human, motorcycle and bicycle in surveillance video was presented by using machine learning idea. The method can be described as three steps: feature selection, training of Support Vector Machine(SVM) classifier and performance evaluation. Firstly, a feature vector to represent the discriminabilty of an object is described. From the profile of object, the ratio of width to height and trisection ratio of width to height are firstly adopted as the distinct feature. Moreover, we use external rectangle to approximate the object mask, which leads to a feature of rectangle degree standing for the ratio between the area of object to the area of external rectangle. To cope with the invariance to scale, rotation and so on, Hu moment invariants, Fourier descriptor and dispersedness were extracted as another kind of features. Secondly, a multi-class classifier were designed based on two-class SVM. The idea behind the classifier structure is that the multi-class classification can be converted to the combination of two-class classification. For our case, the final classification is the vote result of six twoclass classifier. Thirdly, we determine the precise feature selection by experiments. According to the classification result, we select different features for each two-class classifier. The true positive rate, false positive rate and discriminative index are taken to evaluate the performance of the classifier. Experimental results show that the classifier achieves good classification precision for the real and test data.
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Airong Sun, Airong Sun, Min Bai, Min Bai, Yihua Tan, Yihua Tan, Jinwen Tian, Jinwen Tian, } "SVM based classification of moving objects in video", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749607 (30 October 2009); doi: 10.1117/12.832622; https://doi.org/10.1117/12.832622
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