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Pedestrian detection is paramount for advanced driver assistance systems (ADAS) and autonomous driving. As a key technology in computer vision, it also finds many other applications, such as security and surveillance etc. Generally, pedestrian detection is conducted for images in visible spectrum, which are not suitable for night time detection. Infrared (IR) or thermal imaging is often adopted for night time due to its capability of capturing the emitted energy from pedestrians. The detection process firstly extracts candidate pedestrians from the captured IR image. Robust feature descriptors are formulated to represent those candidates. A binary classification of the extract features is then performed with trained classifier models. In this paper, an algorithm for pedestrian detection from IR image is proposed, where an adaptive fuzzy C-means clustering and convolutional neural networks are adopted. The adaptive …