Near infrared nighttime road pedestrians recognition based on convolutional neural network

Abstract Pedestrian recognition is the core technology of pedestrian detection in pedestrian protection systems. This paper compares and analyzes, visible and infrared images obtained via visible-spectrum, near-infrared, short-wave infrared, and long-wave infrared cameras. The results show that near-infrared camera was the best for nighttime pedestrian detection when device cost and pedestrian imaging quality were considered. This paper reports on the first time use of a self-learning softmax with a 9-layer Convolutional Neural Network (CNN) model to identify near-infrared nighttime pedestrians. 267,000 samples obtained from the near-infrared images were employed to optimize the CNN recognition model. Collected near-infrared nighttime samples had 3 categories (background, pedestrian, and cyclist or motorcyclist) and will be made publicly available for researchers use. Testing results indicated that the optimized CNN model using self-learning softmax had a competitive accuracy and potential in real-time pedestrian recognition.