Real-time Pedestrian Detection in a Truck's Blind Spot Camera

In this paper we present a multi-pedestrian detection and tracking framework targeting a specific application: detecting vulnerable road users in a truck’s blind spot zone. Research indicates that existing non-vision based safety solutions are not able to handle this problem completely. Therefore we aim to develop an active safety system which warns the truck driver if pedestrians are present in the truck’s blind spot zone. Our system solely uses the vision input from the truck’s blind spot camera to detect pedestrians. This is not a trivial task, since the application inherently requires real-time operation while at the same time attaining very high accuracy. Furthermore we need to cope with the large lens distortion and the extreme viewpoints introduced by the blind spot camera. To achieve this, we propose a fast and efficient pedestrian detection and tracking framework based on our novel perspective warping window approach. To evaluate our algorithm we recorded several realistically simulated blind spot scenarios with a genuine blind spot camera mounted on a real truck. We show that our algorithm achieves excellent accuracy results at real-time performance, using a single core CPU implementation only.