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Learning for classification of traffic-related object on RGB-D data

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Abstract

In order to detect and recognize the traffic-related object, a learning-based classification approach is proposed on RGB-D data. Since RGB-D data can provide the depth information and thus make it capable of tackling the baffling issues such as overlapping, clustered background, the depth data obtained by Microsoft Kinect sensor is introduced in the proposed method for efficiently detecting and extracting the objects in the traffic scene. Moreover, we construct a feature vector, which combine the histograms of oriented gradients, 2D features and 3D Spin Image features, to represent the traffic-related objects. The feature vector is used as the input of the random forest for training a classifier and classifying the traffic-related objects. In experiments, by conducting efficiency and accuracy tests on RGB-D data captured in different traffic scenarios, the proposed method performs better than the typical support vector machine method. The results show that traffic-related objects can be efficiently detected, and the accuracy of classification can achieve higher than 98 %.

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Acknowledgments

This paper draws on work supported in part by the following funds: National High Technology Research and Development Program of China (863 Program) under Grant number 2011AA010101, National Natural Science Foundation of China under Grant number 61002009 and 61304188, Key Science and Technology Program of Zhejiang Province of China under Grant number 2012C01035-1, and Zhejiang Provincial Natural Science Foundation of China under Grant number LZ13F020004 and LR14F020003.

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  1. Hangzhou Normal University, Hangzhou, China

    Yingjie Xia, Xingmin Shi & Na Zhao

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  1. Yingjie Xia
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  2. Xingmin Shi
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  3. Na Zhao
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Correspondence to Xingmin Shi.

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Xia, Y., Shi, X. & Zhao, N. Learning for classification of traffic-related object on RGB-D data. Multimedia Systems 23, 129–138 (2017). https://doi.org/10.1007/s00530-014-0427-4

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