Developing an On-Road Obstacle Detection System Using Monovision

In this study, an onboard camera are used to develop a frontal object detection algorithm for a forward collision warning system. The vision-based object recognition system employs two-stage classifiers to detect and recognize the objects in front of vehicle. Two-stage detection algorithm is adopted to accelerate the computation and increase the recognition accuracy of the algorithm. The detected objects include pedestrians, motorcycles, and cars. Finally, different environmental conditions (daytime and nighttime) were selected to verify the performance of the proposed algorithm. The proposed system achieved detection rates and the false alarm rates of approximately 81.1% and 0.3%, respectively.