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Crowd counting in public video surveillance by label distribution learning
Zhaoxiang Zhang; Mo Wang; Xin Geng
Source PublicationNeurocomputing
2015-10-20
Volume166Issue:1Pages:151-163
AbstractThe increase of population causes the raise of security threat in crowed environment, which makes crowd counting becoming more and more important. For common complexity scenes, existing crowd counting approaches are mainly based on regression models which learn a mapping between low-level features and class labels. One of the major challenges for generating a good regression function is the insufficient and imbalanced training data. Observationally, the problem of crowd counting has the characteristic that crowd images with adjacent class labels contain similar features, which can be utilized to reduce the effect of insufficiency and imbalance by the strategy of information reuse. Consequently, this paper introduces a label distribution learning (LDL) strategy into crowd counting, where crowd images are labelled with label distributions instead of the conventional single labels. In this way, one crowd instance can contribute to not only the learning of its real class label, but also the learning of neighboring class labels. Hence the training data are increased significantly, and the classes with insufficient training data are supplied with more training data, which belongs to their adjacent classes. Experimental results prove the effectiveness and robustness of the LDL method for crowd counting. We have also shown the outstanding performance of the approach in different dataset.
KeywordCrowd Counting Information Reuse Label Distributions Visual Surveillance
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13201
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Zhaoxiang Zhang,Mo Wang,Xin Geng. Crowd counting in public video surveillance by label distribution learning[J]. Neurocomputing,2015,166(1):151-163.
APA Zhaoxiang Zhang,Mo Wang,&Xin Geng.(2015).Crowd counting in public video surveillance by label distribution learning.Neurocomputing,166(1),151-163.
MLA Zhaoxiang Zhang,et al."Crowd counting in public video surveillance by label distribution learning".Neurocomputing 166.1(2015):151-163.
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