A Real-Time Deep Network for Crowd Counting

TL;DR

This paper introduces a compact deep neural network for crowd counting that balances high accuracy with real-time speed and resource efficiency, suitable for practical applications.

Contribution

A novel lightweight CNN architecture with parallel filters enabling fast crowd counting without sacrificing accuracy.

Findings

Achieves nearly real-time processing speed.

Outperforms existing lightweight models in speed.

Balances performance and efficiency effectively.

Abstract

Automatic analysis of highly crowded people has attracted extensive attention from computer vision research. Previous approaches for crowd counting have already achieved promising performance across various benchmarks. However, to deal with the real situation, we hope the model run as fast as possible while keeping accuracy. In this paper, we propose a compact convolutional neural network for crowd counting which learns a more efficient model with a small number of parameters. With three parallel filters executing the convolutional operation on the input image simultaneously at the front of the network, our model could achieve nearly real-time speed and save more computing resources. Experiments on two benchmarks show that our proposed method not only takes a balance between performance and efficiency which is more suitable for actual scenes but also is superior to existing light-weight models in speed.