Pedestrian detection in the surroundings of a vehicle is highly desirable to avoid dangerous traffic situations. Typical vision-based pedestrian detection algorithms on mobile cameras suffer from the lack of a-priori knowledge on the object to be detected. The variability in the shape, pose, color distribution, and behavior affect the robustness of the detection process. A novel vision-based system is proposed to detect pedestrians with a single mobile camera collaborating with a fixed camera observing the same scene. Nowadays, a large number of fixed cameras are installed in major cities. This work presents how features extracted from those fixed cameras can be used to detect pedestrians with mobile cameras present in the same scene. The proposed system outperforms state-of-the-art single frame pedestrian detectors using a feature-based classification framework. In addition, the system can be generalized to any object of interest. Any object detected by a fixed camera, can be detected with a mobile camera.