Lightweight Dual-Task Networks For Crowd Counting In Aerial Images

Tian, Ye; Duan, Chengzhen; Zhang, Ruilin; Wei, Zhiwei; Wang, Hongpeng

doi:10.1109/icassp39728.2021.9413949

Cited by 3 publications

(2 citation statements)

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“…Compared to the PPAM block in our model, our method takes fewer computations to process scaleaware information and the Swin-Transformer block is more effective in extracting global context information. Tian et al [41] and Zhang et al [42] put forward guidance branch to their lightweight model to learn localization information in their work; such a technique needs precise head location coordinates to guide location task and that is not mandatory in lightweight crowd-counting tasks. When it comes to hybrid network architecture, Sun et al [43] introduce Transformer blocks after each downscale convolution block to separately model scale-varied information stage by stage; it is not computationally efficient to introduce multiple Transformer blocks for the same thing.…”

Section: Lightweight Crowd-counting Modelsmentioning

confidence: 99%

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A Weakly Supervised Hybrid Lightweight Network for Efficient Crowd Counting

Chen,

Zhao,

Gao

et al. 2024

Electronics

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Crowd-counting networks have become the mainstream method to deploy crowd-counting techniques on resource-constrained devices. Significant progress has been made in this field, with many outstanding lightweight models being proposed successively. However, challenges like scare-variation, global feature extraction, and fine-grained head annotation requirements still exist in relevant tasks, necessitating further improvement. In this article, we propose a weakly supervised hybrid lightweight crowd-counting network that integrates the initial layers of GhostNet as the backbone to efficiently extract local features and enrich intermediate features. The incorporation of a modified Swin-Transformer block addresses the need for effective global context information. A Pyramid Pooling Aggregation Module handles the inherent scale variation problem in crowd-counting tasks in a more computation-efficient way. This module, along with the cross-attention module, serves as bridges to promote the feature information flow between local features and global context information. Finally, a simplified regressor module is designed to allow the proposed model with weakly supervised guidance for training to avoid precise location-level annotations, noting that the omission of density map generation makes the proposed network more lightweight. Our results on the UCF-QNRF dataset indicate our model is 8.73% and 12.17% more accurate on MAE and MSE metrics, respectively, than the second-best ARNet, in which the parameters decrease by 4.52%. On the ShanghaiTech A dataset, MAE and MSE drop 1.5% and 3.2%, respectively, compared to the second-best PDDNet. The experimental results for accuracy and inference speed evaluation on some mainstream datasets validate the effective design principle of our model.

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Section: Lightweight Crowd-counting Modelsmentioning

confidence: 99%

“…Just like previous work [15,39,41], we simply use mean absolute error (MAE) and mean square error (MSE) to evaluate our model; they are defined respectively as:…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Weakly Supervised Hybrid Lightweight Network for Efficient Crowd Counting

Chen,

Zhao,

Gao

et al. 2024

Electronics

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Adaptive Scale Selection Network for Crowd Counting

Lai,

Liu,

Liu

2023

2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)

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On-Board Crowd Counting and Density Estimation Using Low Altitude Unmanned Aerial Vehicles—Looking beyond Beating the Benchmark

et al. 2022

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Recent advances in deep learning-based image processing have enabled significant improvements in multiple computer vision fields, with crowd counting being no exception. Crowd counting is still attracting research interest due to its potential usefulness for traffic and pedestrian stream monitoring and analysis. This study considered a specific case of crowd counting, namely, counting based on low-altitude aerial images collected by an unmanned aerial vehicle. We evaluated a range of neural network architectures to find ones appropriate for on-board image processing using edge computing devices while minimising the loss in performance. Through experiments on a range of neural network architectures, we also showed that the input image resolution significantly impacts the prediction quality and should be considered an important factor before going for a more complex neural network model to improve accuracy. Moreover, by extending a state-of-the-art benchmark with more in-depth testing, we showed that larger models might be prone to overfitting because of the relative scarcity of training data.

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Lightweight Dual-Task Networks For Crowd Counting In Aerial Images

Cited by 3 publications

References 20 publications

A Weakly Supervised Hybrid Lightweight Network for Efficient Crowd Counting

A Weakly Supervised Hybrid Lightweight Network for Efficient Crowd Counting

Adaptive Scale Selection Network for Crowd Counting

On-Board Crowd Counting and Density Estimation Using Low Altitude Unmanned Aerial Vehicles—Looking beyond Beating the Benchmark

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