Abstract
Aiming at the problems of crowd distribution, scale feature, and crowd feature extraction difficulties in exhibition centers, this paper proposes a crowd density estimation method using deep learning for passenger flow detection systems in exhibition centers. Firstly, based on the pixel difference symbol feature, the difference amplitude feature and gray feature of the central pixel are extracted to form the CLBP feature to obtain more crowd group description information. Secondly, use the LR activation function to add nonlinear factors to the convolution neural network (CNN) and use dense blocks derived from crowd density estimation to train the LR-CNN crowd density estimation model. Finally, experimental results show that the mean absolute error (MAE) and mean square error (MSE) of the proposed method in the UCF_CC_50 dataset are 325.6 and 369.4, respectively. Besides, MAE and MSE in part_A of the Shanghai Tech dataset are 213.5 and 247.1, respectively, and they in part_B are 85.3 and 99.7, respectively. The proposed method effectively improves the accuracy of crowd density estimation in exhibition centers.
Highlights
(3) Virtual model construction: it provides a reliable mathematical model for the transformation between virtual reality and reality [12]
Based on the above analysis, this paper proposes a crowd density estimation method using deep learning for passenger flow detection systems in exhibition centers in order to solve the problems of crowd distribution, scale feature and crowd feature extraction difficulty in the exhibition center scene
In the use of the complete local binary pattern (CLBP) feature extraction algorithm and the convolution neural network (CNN) depth model for crowd density estimation and group detection, this paper has carried out 2000 iterations of training
Summary
(3) Virtual model construction: it provides a reliable mathematical model for the transformation between virtual reality and reality [12]. Crowd counting and density estimation research cannot only provide important guarantees for the safety of people’s lives and property and aid in promoting the maximization of social and economic benefits. Reference [21] designed a multitask framework based on CNN to simultaneously estimate the density level and the number of target crowds It used the former to provide additional information to assist the latter to improve the counting performance of the model. Reference [23] used the same network to process and generate crowd density maps for input images at different resolutions, and at the same time, output attention maps to supervise the generation of crowd scale predictions. Reference [27] proposed a crowd counting method based on crossconfrontation loss and global features for high-density scenes of different scales. When the background is more complex, crowd density estimation is more difficult, and the extraction of scale features and crowd features is not sufficient
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
