DocumentCode :
578332
Title :
Air-ground vehicle detection using local feature learning and saliency region detection
Author :
Xu, Qinghan ; Jin, Lizuo ; Jie, Feiran ; Fei, Shumin
Author_Institution :
Sch. of Autom., Southeast Univ., Nanjing, China
fYear :
2012
fDate :
6-8 July 2012
Firstpage :
4726
Lastpage :
4731
Abstract :
Moving vehicle detection is very important for urban traffic surveillance and situational awareness on the battlefield. Algorithms with cascade structure like Adaboost are booming in the recent decade, and successful in realtime application. However, most of them use a sliding window protocol on multi-scale images which involves heavy computing. Therefore, they are only suitable for simple feature. In this paper, a biologically inspired method is proposed. We learn patch-based features for vehicle detection by unsupervised learning, and then employ a visual saliency step after feature extraction. Instead of sliding window, a candidate region is sent to classifier only if its features are “salient” on whole image. As the number of candidate regions decreases dramatically, it allow us to utilize complex feature to increase description ability. Experimental result indicates less computational expense and good performance.
Keywords :
aerospace computing; feature extraction; learning (artificial intelligence); military computing; space vehicles; video surveillance; Adaboost; air ground vehicle detection; battlefield; cascade structure; feature extraction; local feature learning; multiscale images; saliency region detection; sliding window protocol; unsupervised learning; urban traffic surveillance; Feature extraction; Support vector machines; Unsupervised learning; Vectors; Vehicle detection; Vehicles; Visualization; saliency detection; unsupervised learning; vehicle detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1397-1
Type :
conf
DOI :
10.1109/WCICA.2012.6359374
Filename :
6359374
Link To Document :
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