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 DE-TRAC 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. Index Terms-Vehicle detection, deep neural networks
Wang et al. REF design a light-weight proposal network with a fine-tuning network for traffic surveillance.
Evolving Boxes for fast Vehicle Detection
IEEE International Conference on Multimedia and Expo (ICME), 2017, pp. 1135-1140
Computer Science