3D-DETNet: a Single Stage Video-Based Vehicle Detector
This work addresses vehicle detection in videos for applications like autonomous driving, but it is incremental as it builds on existing single-stage methods by incorporating temporal information.
The authors tackled video-based vehicle detection by proposing 3D-DETNet, a single-stage detector using 3D convolution and focal loss, which achieved state-of-the-art performance on the UA-DETAC dataset with a detection speed of 26 fps.
Video-based vehicle detection has received considerable attention over the last ten years and there are many deep learning based detection methods which can be applied to it. However, these methods are devised for still images and applying them for video vehicle detection directly always obtains poor performance. In this work, we propose a new single-stage video-based vehicle detector integrated with 3DCovNet and focal loss, called 3D-DETNet. Draw support from 3D Convolution network and focal loss, our method has ability to capture motion information and is more suitable to detect vehicle in video than other single-stage methods devised for static images. The multiple video frames are initially fed to 3D-DETNet to generate multiple spatial feature maps, then sub-model 3DConvNet takes spatial feature maps as input to capture temporal information which is fed to final fully convolution model for predicting locations of vehicles in video frames. We evaluate our method on UA-DETAC vehicle detection dataset and our 3D-DETNet yields best performance and keeps a higher detection speed of 26 fps compared with other competing methods.