文章基本信息

标题：Saliency Analysis for Car Detection Based on 2-D Entropy and Velocity Prior
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作者：Xiaolong Ma ; Xiaolong Ma ; Xudong Xie 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2014
卷号：138
页码：378-385
DOI：10.1016/j.sbspro.2014.07.216
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, an efficient saliency analysis method based on entropy theory and wavelet analysis is proposed, which can be used for car detection in traffic video. In our method, the wavelet theory is used to detect the global saliency parts on a single frame in the video. The entropy theory is applied to choose the best saliency map among three color channels. Besides, the use of velocity information can enhance the objects close to observers, which is consistent with humans’ visual habit. Experimental results show that our method can achieve excellent results in terms of receiver operating characteristic (ROC) curve, the area under the curve (AUC) score, linear correlation coefficient (CC) score, and normalized scanpath saliency (NSS) score, as compared to other state-of-the-art methods.
关键词：Saliency analysis;Car detection;2-D entropy;Wavelet analysis;Velocity prior