TY - GEN
T1 - Fully optimized convolutional neural network based on small-scale crowd
AU - Deng, Lijia
AU - Wang, Shui Hua
AU - Zhang, Yu Dong
N1 - Publisher Copyright:
© 2020 IEEE
PY - 2020
Y1 - 2020
N2 - Crowd counting is of considerable significance to society in terms of public safety and urban development. Manual counting of people in a video or photo is often time-consuming and labour-intensive. People will need an efficient and economy way instead of counting manually. Nowadays, the convolutional neural network was popularly utilized as the baseline for crowd counting. However, the more complex the CNN-based algorithm, the more computing resources will be consumed. This article aims to present a simpler and faster fully optimized convolutional neural network for crowd counting with desired performance. To minimize the computational cost on training networks, we proposed a fully optimized method to build our network. Extensive experiments on our fully optimized convolutional neural network indicate the superiority of our network that has very high accuracy and speed on small scale crowd.
AB - Crowd counting is of considerable significance to society in terms of public safety and urban development. Manual counting of people in a video or photo is often time-consuming and labour-intensive. People will need an efficient and economy way instead of counting manually. Nowadays, the convolutional neural network was popularly utilized as the baseline for crowd counting. However, the more complex the CNN-based algorithm, the more computing resources will be consumed. This article aims to present a simpler and faster fully optimized convolutional neural network for crowd counting with desired performance. To minimize the computational cost on training networks, we proposed a fully optimized method to build our network. Extensive experiments on our fully optimized convolutional neural network indicate the superiority of our network that has very high accuracy and speed on small scale crowd.
KW - Convolutional neural network
KW - Crowd counting
KW - Fully convolutional neural network
KW - Fully optimized convolutional neural network
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85109341724&partnerID=8YFLogxK
M3 - Conference Proceeding
AN - SCOPUS:85109341724
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
Y2 - 10 October 2020 through 21 October 2020
ER -