Optimizing Crowd Counting on Low Compute Device Using Heterogeneous Convolution Filter

Optimizing Crowd Counting on Low Compute Device Using Heterogeneous Convolution Filter

© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Christopher Alvin, Gede Putra Kusuma

How to Cite?

Christopher Alvin, Gede Putra Kusuma, "Optimizing Crowd Counting on Low Compute Device Using Heterogeneous Convolution Filter," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 66-74, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P208

The massive growth of population increases potential security risks in the crowded area. To monitor these phenomena, crowd counting can be one of the solutions. But existing crowd counting approach still requires a high compute device. In this work, a mobile device focused density-based crowd counting is proposed. To tackle high resource consumption, the proposed model reduces the number of parameters by using a Heterogeneous Convolution Filter, resulting in lower computation and faster counting time. The experiment is done on three datasets, the ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50. Extensive experiments are conducted on mobile devices to have an overview of the performance of the model on mobile devices. The performance of the proposed model compared to CSRNet on mobile are similar on each dataset by only having a bigger MAE by 0.64%, 3%, and 10.56% on UCF_CC_50, ShanghaiTech Part A, and ShanghaiTech Part B, respectively but with better latency by 29.83%, 44.85%, 50%, respectively, and better battery consumption by 25.71%, 44.17% and 50.79% respectively. The proposed model successfully improves the speed of counting the number of people in an image, with a slightly higher MAE compared to the CSRNet on mobile devices.

Crowd Counting, Convolutional Neural Network, Heterogeneous Convolution Filter, Deep Learning, Mobile Computing.

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