A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting

• URL: http://arxiv.org/abs/2401.05968v1
• Date: Thu, 11 Jan 2024 15:13:31 GMT
• Title: A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting
• Authors: Yashwardhan Chaudhuri, Ankit Kumar, Orchid Chetia Phukan, Arun Balaji Buduru
• Abstract summary: We introduce two lightweight models to enhance the versatility of crowd-counting 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.
• Score: 3.5066463427087777
• License: http://creativecommons.org/licenses/by/4.0/
• 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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