Saliency Analysis for Car Detection Based on 2-D Entropy and Velocity Prior
In 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 the authors' 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 the authors' 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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/18770428
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Ma, Xiaolong
- Xie, Xudong
- Hu, Jianming
- Zhang, Yi
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Conference:
- 9th International Conference on Traffic and Transportation Studies (ICTTS’2014)
- Location: Shaoxing Zhejiang Province, China
- Date: 2014-8-1 to 2014-8-2
- Publication Date: 2014-7-14
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 378-385
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Serial:
- Procedia - Social and Behavioral Sciences
- Volume: 138
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1877-0428
- Serial URL: http://www.sciencedirect.com/science/journal/18770428/53
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Detection and identification; Entropy (Statistical mechanics); Vehicles; Velocity; Video imaging detectors; Wavelets
- Subject Areas: Highways; Planning and Forecasting; Vehicles and Equipment; I72: Traffic and Transport Planning; I90: Vehicles;
Filing Info
- Accession Number: 01535339
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 27 2014 10:47AM