Efficient multiclass object detection: Detecting pedestrians and bicyclists in a truck´s blind spot camera

In this paper we propose an efficient detection and tracking framework targeting vulnerable road users in the blind spot camera images of a truck. Existing non-vision based safety solutions are not able to handle this problem completely. Therefore we aim to develop an active safety system, based solely on the vision input of the blind spot camera. This is far from trivial: vulnerable road users are a diverse class and consist of a wide variety of poses and appearances. Evidently we need to achieve excellent accuracy results and furthermore we need to cope with the large lens distortion and extreme viewpoints induced by the blind spot camera. In this work we present a multiclass detection methodology which enables the efficient detection of both pedestrians and bicyclists in these challenging images. To achieve this we propose the integration of a warping window approach with multiple object detectors which we intelligently combine in a probabilistic manner. To validate our framework we recorded several simulated dangerous blind spot scenarios with a genuine blind spot camera mounted on a real truck. We show that our approach achieves excellent accuracy on these challenging datasets.