Abstract
Pedestrian detection is a special topic in computer vision and plays a key role in intelligent vehicles and unmanned drive. Although recent pedestrian detect methods such as RPN_BF [1] have shown good performance from visible spectrum images at daytime, they have limited study for near-infrared image at nighttime. Unfortunately, when the traffic accident happened at night, the pedestrian is one of the most serious victims. Recently deep convolutional neural networks such as R-CNN/Faster R-CNN [2, 3] have shown excellent performance for object detection. In this paper, we investigate issues involving Faster R-CNN for construction of end-to-end all-day pedestrian detection system. We propose an effective baseline for pedestrian detection both on visible spectrum images and infrared images, using a same pre-train Faster R-CNN model. We comprehensively evaluate this method, the experiment results presenting competitive accuracy and acceptable running time.
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Zhang, X., Chen, G., Saruta, K., Terata, Y. (2017). Deep Convolutional Neural Networks for All-Day Pedestrian Detection. In: Kim, K., Joukov, N. (eds) Information Science and Applications 2017. ICISA 2017. Lecture Notes in Electrical Engineering, vol 424. Springer, Singapore. https://doi.org/10.1007/978-981-10-4154-9_21
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DOI: https://doi.org/10.1007/978-981-10-4154-9_21
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