科研之友
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摘要
With the continuous improvement of image resolution, details on aerial images provide abundant available information for vehicle detection. Nevertheless, traditional works mainly exploited the overall information of the vehicles ignoring the local details, such as front and rear windshields, and thus, there were usually more than 15% false alarms in the final vehicle detection results. In this letter, we propose a vehicle detection method making full use of high level details on aerial images. In the training stage, we choose front windshield samples to train a part detector and whole vehicle samples to train a root detector. In the matching stage, we first use the root detector to define an entire vehicle obtaining the root response, then the part detector is scanned in the root bounding box to decide a front windshield and get the part response. Afterward, the part response is transformed by setting weight w based on the part position offset. More importantly, contextual information is appropriately used in the process of determining the part position offset. Final detection score is the combination of root response and the transformed part response. We have demonstrated that the proposed method has achieved better performance with more than 6.43% increase of correct detection rate and more than 5.63% decrease of false detection rate compared with the state-of-the-art approaches.
- 出版日期2017-8
- 单位中南大学
