A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting

Yashwardhan Chaudhuri; Ankit Kumar; Orchid Chetia Phukan; Arun Balaji Buduru

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arXiv · 2401.05968

A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting

Yashwardhan Chaudhuri·Ankit Kumar·Orchid Chetia Phukan·Arun Balaji Buduru

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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.

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Yashwardhan Chaudhuri, Ankit Kumar, Orchid Chetia Phukan, Arun Balaji Buduru. 2024-01-11. A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting. https://arxiv.org/abs/2401.05968

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