Evolving Boxes for Fast Vehicle Detection

Li Wang1,2, Yao Lu3, Hong Wang2, Yingbin Zheng2, Hao Ye2, Xiangyang Xue1

1School of Computer Science, Fudan University      2Shanghai Advanced Research Institute, CAS

3Department of Computer Science and Engineering, University of Washington


We perform fast vehicle detection from traffic surveillance cameras. The classic cascade object detection is revisited and a novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by apply different feature fusion techniques, the initial boxes can be refined in terms of both localization and recognition, leading to evolved boxes.

We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9-13 FPS detection speed on a moderate commercial GPU.

Related Publication

Li Wang, Yao Lu, Hong Wang, Yingbin Zheng, Hao Ye, Xiangyang Xue, Evolving Boxes for Fast Vehicle Detection. In IEEE International Conference on Multimedia and Expo (ICME), 2017. [pdf][code][detection results]