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 fuzzy C-means clustering is used to segment the IR images and retrieve the candidate pedestrians. The candidate pedestrians are then pruned using human posture characteristics and the second central moments ellipse. The convolutional neural network is used to simultaneously learn relevant features and perform the binary classification. The performance of the proposed algorithm is compared with state-of-the-art algorithms on publicly available data set. A better detection accuracy with reduced computational accuracy is achieved.
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