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

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 %.