Object Detection, Classification and Localization by Infrastructural Stereo Cameras
In the future, autonomously driving vehicles have to navigate in challenging environments. In some situations, their perception capabilities are not able to generate a reliable overview of the environment, by reason of occlusions. In this contribution, an infrastructural stereo camera system for environment perception is proposed. Similar existing systems only detect moving objects by background subtraction algorithms and monocular cameras. In contrast, the proposed approach fuses three different algorithms for object detection and classification and uses stereo vision for object localization. The algorithmic concept is composed of a background subtraction algorithm based on Gaussian Mixture Models, the convolutional neural network "You only look once" as well as a novel algorithm for detecting salient objects in depth maps. The combination of these complementary object detection principles allows the reliable detection of dynamic as well as static objects. An algorithm for fusing the results of the three object detection methods based on bounding boxes is introduced. The proposed fusion algorithm for bounding boxes improves the detection results and provides an information fusion. We evaluate the proposed concept on real word data. The object detection, classification and localization in the real world scenario is investigated and discussed.
VISIGRAPP