Traffic sign detection from lower-quality and noisy mobile videos

Accurate traffic sign detection, from vehicle-mounted cameras, is an important task for autonomous driving and driver assistance. It is a challenging task especially when the videos acquired from mobile cameras on portable devices are lowquality. In this paper, we focus on naturalistic videos captured from vehicle-mounted cameras. It has been shown that Regionbased Convolutional Neural Networks provide high accuracy rates in object detection tasks. Yet, they are computationally expensive, and often require a GPU for faster training and processing. In this paper, we present a new method, incorporating Aggregate Channel Features and Chain Code Histograms, with the goal of much faster training and testing, and comparable or better performance without requiring specialized processors. Our test videos cover a range of different weather and daytime scenarios. The experimental results show the promise of the proposed method and a faster performance compared to the other detectors.