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2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (2017)
Lecce, Italy
Aug. 29, 2017 to Sept. 1, 2017
ISBN: 978-1-5386-2940-6
pp: 1-6
Haiying Jiang , School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China
Weidong Jin , School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China
Zhibin Yu , School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China
Peizhen Xu , School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China
ABSTRACT
In this paper, we present a new approach to count the number of people that cross a counting line from video images. This paper focuses on point-level annotation in training images and incorporate spatial features along with novel temporal features in training the structured random forest for estimating crowd density. By computing the crowd velocity, we model the crowd counting map as elementwise multiplication of crowd density map and crowd velocity map. Integrating over crowd counting map on the line of interest(LOI) locations leads to the instantaneous LOI counting numbers. We show that results are comparable to those obtained when using more complex and costly techniques.
INDEX TERMS
Optical imaging, Adaptive optics, Estimation, Feature extraction, Training, Vegetation, Optical variables measurement
CITATION

H. Jiang, W. Jin, Z. Yu and P. Xu, "Combing spatial and temporal features for crowd counting with point supervision," 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy, 2017, pp. 1-6.
doi:10.1109/AVSS.2017.8078489
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