Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks
Robust vision based pedestrian detection is a crucial feature of future autonomous systems. Thermal cameras provide an additional input channel that helps solving this task and deep convolutional networks are the currently leading approach for many pattern recognition problems, including object detection. In this paper, we explore the potential of deep models for multispectral pedestrian detection. We investigate two deep fusion architectures and analyze their performance on multispectral data. Our results show that a pre trained late fusion architecture significantly outperforms the current state of the art ACF+T+THOG solution.
ESANN