Robust Real-time Pedestrians Detection in Urban Environments with a Network of Low Resolution Cameras
Detecting that pedestrians are present in front of a vehicle is highly desirable to avoid dangerous traffic situations. A novel vision-based system is presented to automatically detect far-away pedestrians with low-resolution cameras mounted in vehicles given the contributions of fixed cameras present in the scene. Fixed cameras detect pedestrians by solving an inverse problem built upon a multi-class dictionary of atoms approximating the foreground silhouettes. A sparse-sensing strategy is proposed to extract the foreground silhouettes and classify them in real-time. Mobile cameras detect pedestrians given only their appearance in the fixed cameras. A cascade of compact binary strings is presented to model the appearance of pedestrians and match them across cameras. The proposed system addresses the practical requirements of transportation systems: it runs in real-time with low memory loads and bandwidth consumption. We evaluate the performance of our system when extracted features are severely degraded and the sensing devices are of low quality. Experimental results demonstrate the feasibility of our collaborative vision-based system.