DocumentCode
3579707
Title
Online Multiperson Tracking and Counting with Cloud Computing
Author
Weishan Zhang ; Wenshan Wang ; Pengcheng Duan ; Xin Liu ; Qinghua Lu
Author_Institution
Dept. of Software Eng., China Univ. of Pet., Qingdao, China
fYear
2014
Firstpage
72
Lastpage
75
Abstract
Intelligent video surveillance is a challenging issue due to complicated scenes. Based on empirical and experimental explorations, we propose a multi-person tracking-by-detection framework to achieve pedestrian counting at run time. This framework is integrated with a stream based cloud computing paradigm to improve tracking performance. We evaluated our approach which shows improved time performance compared with those classical approaches.
Keywords
cloud computing; knowledge based systems; object detection; object tracking; pedestrians; video surveillance; intelligent video surveillance; online multiperson counting; online multiperson tracking-by-detection framework; pedestrian counting; stream based cloud computing; tracking performance; Algorithm design and analysis; Cloud computing; Detectors; Kalman filters; Storms; Streaming media; Support vector machines; HOG; Kalman filter; SVM; cloud computing; multiperson tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Identification, Information and Knowledge in the Internet of Things (IIKI), 2014 International Conference on
Type
conf
DOI
10.1109/IIKI.2014.22
Filename
7064001
Link To Document