  We perform fast vehicle detection from traffic surveillance cameras. 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 applying different feature fusion techniques, the
initial boxes can be refined for both localization and recognition. 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.
