Integrating 3D structure into traffic scene understanding with RGB-D data

RGB Video now is one of the major data sources of traffic surveillance applications. In order to detect the possible traffic events in the video, traffic-related objects, such as vehicles and pedestrians, should be first detected and recognized. However, due to the 2D nature of the RGB videos, there are technical difficulties in efficiently detecting and recognizing traffic-related objects from them. For instance, the traffic-related objects cannot be efficiently detected in separation while parts of them overlap, and complex background will influence the accuracy of the object detection. In this paper, we propose a robust RGB-D data based traffic scene understanding algorithm. By integrating depth information, we can calculate more discriminative object features and spatial information can be used to separate the objects in the scene efficiently. Experimental results show that integrating depth data can improve the accuracy of object detection and recognition. We also show that the analyzed object information plus depth data facilitate two important traffic event detection applications: overtaking warning and collision