A temporal-based deep learning method for multiple objects detection in autonomous driving

摘要

This paper proposes a novel vision-based object detection method in autonomous driving, which introduces the temporal information into the deep learning-based detection method for moving object detection. Vision-based object detection is a critical technology for autonomous driving. The objects in the real world such as driving cars, don't have great changes in their positions and velocities. So the position change of objects between two consecutive frames is not large. This is usually ignored by traditional works, which usually use object detection methods on still-images to detect moving objects. Considering the relationship among consecutive frames (temporal information), we present a robust and real-time tracking method following image detection to refine the object detection results. Based on the three key attributes (distances, sizes and positions), the tracking method aims to build the association between the detected objects on the current frame and those in previous frames. The proposed object detection with temporal information dramatically improves the performance of existing object detection algorithms based on stillimage. With the proposed method, we won the champion in the preceding vehicle detection task in 2017 intelligent vehicle future challenge(2017 IVFC)^1.