Published on Wed Feb 01 2017

Evolving Boxes for Fast Vehicle Detection

Li Wang, Yao Lu, Hong Wang, Yingbin Zheng, Hao Ye, Xiangyang Xue

We perform fast vehicle detection from traffic surveillance cameras. We evaluate our network on the recent DETRAC benchmark. We obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5%.

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Abstract

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.

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