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Targets Association across Multiple Cameras by Learning Transfer Models
Liu Suolan,Wang Jia,Sun Changyin 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.1
In this paper, we propose a novel method to solve the problem of targets association and tracking across multiple cameras of non-overlapping views. The method is divided into two parts. One is an improvement on appearance transfer model, another is an improvement on spatio-temporal transfer model. To learn inter-camera appearance transfer models, lαβ color space is used to calibrate images. By this way, the overall and local information can be used, which has advantage to color transform correction. To learn spatio-temporal transfer model, entry/exit zones of a non-overlapping topology can be effectively estimated by defining valid link and using clustering method. Then a kind of time constrain is set between two nodes to judge whether there is correlation of observations. Experiments show the effectiveness of the proposed method.
Human Activities Recognition Based on Skeleton Information via Sparse Representation
Liu, Suolan,Kong, Lizhi,Wang, Hongyuan Korean Institute of Information Scientists and Eng 2018 Journal of Computing Science and Engineering Vol.12 No.1
Human activities recognition is a challenging task due to its complexity of human movements and the variety performed by different subjects for the same action. This paper presents a recognition algorithm by using skeleton information generated from depth maps. Concatenating motion features and temporal constraint feature produces feature vector. Reducing dictionary scale proposes an improved fast classifier based on sparse representation. The developed method is shown to be effective by recognizing different activities on the UTD-MHAD dataset. Comparison results indicate superior performance of our method over some existing methods.
Human Activities Recognition Based on Skeleton Information via Sparse Representation
Suolan Liu,Lizhi Kong,Hongyuan Wang 한국정보과학회 2018 Journal of Computing Science and Engineering Vol.12 No.1
Human activities recognition is a challenging task due to its complexity of human movements and the variety performed by different subjects for the same action. This paper presents a recognition algorithm by using skeleton information generated from depth maps. Concatenating motion features and temporal constraint feature produces feature vector. Reducing dictionary scale proposes an improved fast classifier based on sparse representation. The developed method is shown to be effective by recognizing different activities on the UTD-MHAD dataset. Comparison results indicate superior performance of our method over some existing methods.