Visual Part-based Vehicle Detection by Integrating Different Discriminative Features for Rear Traffic Scenes

• Main Authors: Yi-Hang Chiu, 邱一航
• Other Authors: Li-Chen Fu
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/76657926329918072122

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 96 === Every year many people are injured and killed in traffic accidents. In order to warn and protect the driver in the potential accidents, we develop a vision system to detect whatever style rear vehicles under various traffic or weather conditions. Otherwise, in the vision system, the incomplete rear vehicle is shown in the image due to the limitation of field of view of the camera. We propose a vision system detecting rear vehicles, whether occluded or not. The system selects and integrates different parts of the rear vehicle pattern; these parts are represented by complementary characteristics. There are two kinds of classifiers for the part selection. First, the appearances of each vehicle’s part with different feature types are evaluated by the representative classifiers. Second, the geometry of the parts is modeled as a spatial classifier; each spatial classifier evaluates the stability of vehicle location estimation of each part. Representative classifiers and the spatial classifiers are learnt and integrated according to the performance of each part, including the discriminabilities of appearance and the stabilities of the geometry estimation. The modified error rate function of a boosting algorithm selects parts of the rear vehicle with complementary discriminative features. These selected parts vote the possible vehicle’s location in the image; the majority votes locate the possible rear vehicles.