Human Detection Using a Mobile Platform and Novel Features Derived From a Visual Saliency Mechanism

Human detection is a key ability to an increasing number of applications that op-erates in human inhabited environments or needs to interact with a human user. Currently, most successful approaches to human detection are based on background substraction techniques that apply only to the case of static cameras or cameras with highly constrained motions. Furthermore, many applications rely on features derived from specific human poses, such as systems based on features derived from the human face which is only visible when a person is facing the detecting camera. In this work, we present a new computer vision algorithm designed to operate with moving cameras and to detect humans in different poses under partial or complete view of the human body. We follow a standard pattern recognition approach based on 4 main steps: i) Preprocessing to achieve color constancy and stereo pair calibra-tion, ii) Segmentation using depth continuity information, iii) Feature extraction based on visual saliency, and iv) Classification using a neural network. The main novelty of our approach lies in the feature extraction step, where we propose novel features derived from a visual saliency mechanism. In contrast to previous works, we do not use a pyramidal decomposition to run the saliency algorithm, but we im-plement this at the the original image resolution using the so-called integral image. Our results indicate that our method: i) Outperforms state-of-the-art techniques for human detection based on face detectors, ii) Outperforms state-of-the-art tech-niques for complete human body detection based on different set of visual features, iii) Operates in real time on-board a mobile platform, such as a mobile robot (15 fps)