A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting
Abstract
Crowd counting finds direct applications in real-world situations, making computational efficiency and performance crucial. However, most of the previous methods rely on a heavy backbone and a complex downstream architecture that restricts the deployment. To address this challenge and enhance the versatility of crowd-counting models, we introduce two lightweight models. These models maintain the same downstream architecture while incorporating two distinct backbones: MobileNet and MobileViT. We leverage Adjacent Feature Fusion to extract diverse scale features from a Pre-Trained Model (PTM) and subsequently combine these features seamlessly. This approach empowers our models to achieve improved performance while maintaining a compact and efficient design. With the comparison of our proposed models with previously available state-of-the-art (SOTA) methods on ShanghaiTech-A ShanghaiTech-B and UCF-CC-50 dataset, it achieves comparable results while being the most computationally efficient model. Finally, we present a comparative study, an extensive ablation study, along with pruning to show the effectiveness of our models.
Keywords
Cite
@article{arxiv.2401.05968,
title = {A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting},
author = {Yashwardhan Chaudhuri and Ankit Kumar and Orchid Chetia Phukan and Arun Balaji Buduru},
journal= {arXiv preprint arXiv:2401.05968},
year = {2024}
}