Pedestrian Detection for Autonomous Vehicle Using Multi-Spectral Cameras

Pedestrian detection is a critical feature of autonomous vehicle or advanced driver assistance system. This paper presents a novel instrument for pedestrian detection by combining stereo vision cameras with a thermal camera. A new dataset for vehicle applications is built from the test vehicle recorded data when driving on city roads. Data received from multiple cameras are aligned using trifocal tensor with pre-calibrated parameters. Candidates are generated from each image frame using sliding windows across multiple scales. A reconfigurable detector framework is proposed, in which feature extraction and classification are two separate stages. The input to the detector can be the color image, disparity map, thermal data, or any of their combinations. When applying to convolutional channel features, feature extraction utilizes the first three convolutional layers of a pre-trained convolutional neural network cascaded with an AdaBoost classifier. The evaluation results show that it significantly outperforms the traditional histogram of oriented gradients features. The proposed pedestrian detector with multi-spectral cameras can achieve 9% log-average miss rate. The experimental dataset is made available at http://computing.wpi.edu/dataset.html.