| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 102 === It is critical to detect bicycles and motorcycles on the road because collision of autos with those light vehicles becomes major cause of on-road accidents nowadays especially in the nighttime. Therefore, a vision-based nighttime on-road bicycle and motorcycle detection method relying on use of a camera and near-infrared lighting mounted on an auto vehicle is proposed in this paper. Generally, the objects will reflect near-infrared lighting. However, some components of the bicycles and the motorcycles absorb most infrared lighting and thus make the bicycles and motorcycles hardly recognizable. To cope with this problem, the aforementioned detection method is part-based, which combines the two kinds of features related to the characteristics of bicycles and motorcycles. Also, the information about the geometric relation among all the parts and the object centroid is learned off-line. Due to high computation load, selection of effective parts with better geometric information is imperative for detection.
On the other hand, cyclist is also an important object to detect. We adopt a two-fold strategies, where one detects the cyclist by a holistic-based detector, and second is to establish a spatial relationship model between the cyclist and his/her riding vehicle off line. In particular, the second strategy filters out the wrong detection. The performance of spatial relationship validation depends on the tightness of bounding box. Hence, we propose a bounding box refinement to refine the detection results. To validate the proposed results, several experiments are conducted to show that the developed system is reliable in detecting bicycles and motorcycles on the road in the nighttime.
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