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RISS 인기검색어
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- 저자 : Xunyuan Zhang (검색결과 2 건)
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Crowded Pedestrian Detection and Density Estimation by Visual Words Analysis
Shilin Zhang,Xunyuan Zhang 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.3
Crowded pedestrian detection and density estimation are very useful and important under transportation environment. In this paper, we present a novel method for crowded pedestrian detection and density estimation through a weighting scheme of bag of visual words model which characterizes both the weight and the relative spatial arrangement aspects of visual words in depicting an image. Firstly, we analyze the visual words generation process. We give each visual word a weight by counting the number of images through which each visual word is clustered and computing the cluster radius of each visual word. To be more specifically, the co-occurrences of visual words are computed with respect to spatial predicates over a hierarchical spatial partitioning of an image. We validate this method using a challenging ground truth pedestrian dataset Pascal VOC 2007. Our approach is shown to be more accuracy than a non-weighting bag-of-visual-words one. The algorithm’s cost is also more efficient than the competing pairs.
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Locate and Detect Persons in Crowded Scenes Aided by Objectiveness Measure
Shilin Zhang,Xunyuan Zhang 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.6
Locating persons in crowded scenes is very difficult due to multi-resolution and complex environment. The other difficulty in pedestrian detection domain is the real time requirement, because the camera installed on the crossing road is in high definition. In this paper, we presented a multi-task pedestrian detection framework boosted by Bing feature. We firstly trained upright full-body, multi-person, half-body and head models, then we compute the object-ness score and generate 1000 proposals by Bing feature, and at last we apply different model to different aspect ratio of the detection proposals. The experiment results on the PASCAL VOC 2007 show that our method outperforms all the other methods and achieved lower miss rate than the state-of-the-art. The computation time cost is just the half of state-of-the-art method.
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