MiCrowd: Vision-Based Deep Crowd Counting on MCU

Son, Sungwook; Seo, Ahreum; Gyeongseon, Eo,; Gill, Kwangyeon; Gong, Taesik; Kim, Hyung-Sin

doi:10.3390/s23073586

Cited by 2 publications

(2 citation statements)

References 26 publications

(44 reference statements)

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“…Lightweight crowd-counting models often implement techniques such as model pruning [1], parameter sharing [2,3], model quantization [4], and knowledge distillation [5] to reduce parameters and computation cost. Sun et al [4] utilized model quantization in their model for a microcontroller unit (MCU), representing a 2.2× speedup compared to the original float model, which indicates its effectiveness in resource-constrained devices. Liu et al [5] proposed a Structure Knowledge Transfer (SKT) framework to allow a student network to learn a feature modeling ability from a teacher network.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

“…However, challenges persist in lightweight crowd-counting network design. First, most of the ones [1,4,[6][7][8][9][10][11] with fully supervised guidance require location-level annotation information in datasets to maintain accurate performance. Generating such annotations is tedious and time-consuming.…”

Section: Introductionmentioning

confidence: 99%