ConNet: Designing a Fast, Efficient, and Robust Crowd Counting Model Through Composite Compression

Abstract

Counting the number of people in a specific area is crucial in maintaining proper crowd safety and management especially in highly-congested indoor scenarios. Recent convolutional neural network (CNN) approaches integrate auxiliary sub-networks to increase the accuracy of the model in estimating crowd size. However, these models require large computational costs due to additional calculations, resulting in an impractically slow inference speed for real-world applications. In this paper, we propose a fast, efficient, and robust crowd counting model called Condensed Network or ConNet. We utilize a composite technique composed of multiple compression methods to reduce the number of parameters of our proposed model. ConNet attains counting accuracy on par with state-of-the-art crowd counting methods on benchmark datasets featuring indoor scenes, while significantly reducing parameter count and increasing inference speed. Moreover, ConNet still performs accurately even with extreme changes in lighting conditions, image resolutions, and camera orientations. Our smallest model ConNet-04 has 61.0× less parameters and is at most 9.0× faster than the baseline approach. Our code and trained models are publicly available at https://github.com/mikatej/ConNet .