Optimising CSRNet with parameter-free attention mechanisms for crowd counting in public transport

📰 ArXiv cs.AI

Optimise CSRNet with parameter-free attention for accurate crowd counting in public transport using deep learning techniques

advanced Published 19 May 2026

Action Steps

Implement CSRNet with parameter-free attention mechanisms to enhance crowd counting accuracy
Train the model on a dataset of images from public transport vehicles with varying occupancy levels
Evaluate the model's performance using metrics such as mean absolute error (MAE) and mean squared error (MSE)
Compare the results with classical models for occupancy estimation
Fine-tune the model by adjusting hyperparameters for optimal performance

Who Needs to Know This

Data scientists and computer vision engineers working on smart public transport systems can benefit from this research to improve occupancy estimation and crowd counting accuracy

Key Insight

💡 Parameter-free attention mechanisms can enhance the accuracy of CSRNet for crowd counting in public transport

Key Takeaways

Optimise CSRNet with parameter-free attention for accurate crowd counting in public transport using deep learning techniques

Full Article

Title: Optimising CSRNet with parameter-free attention mechanisms for crowd counting in public transport

Abstract:
arXiv:2605.18349v1 Announce Type: cross Abstract: Occupancy estimation and crowd counting are critical tasks in designing smart and efficient public transport vehicles. Given that public transport loading can vary from sparse to crowded, classical models for occupancy estimation must be adapted to suit this purpose. Attention mechanisms have shown remarkable capability in enhancing the representational power of deep neural networks for crowd counting in congested scenes with occlusion, complex b

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