Smart City Gnosys
Smart city article details
| Title | Optimizing Crowd Counting In Dense Environments Through Curriculum Learning Training Strategy |
|---|---|
| ID_Doc | 40779 |
| Authors | Fotia L.; Percannella G.; Saggese A.; Vento M. |
| Year | 2024 |
| Published | SN Computer Science, 5, 6 |
| DOI | http://dx.doi.org/10.1007/s42979-024-03029-3 |
| Abstract | Counting individuals in highly crowded environments, characterized by thousands of people, has garnered significant attention in recent years, due to the high number of vertical markets wherein such algorithms can prove beneficial, ranging from smart city and transportation to retail sectors, among others. Within this context, in this paper we introduce a novel training methodology tailored for estimating the number of people, ensuring precise counting accuracy in both moderately and highly crowded scenarios. The proposed approach exploits a formulation of the problem based on point detection, where each point represents an individual’s head. Our innovative contributions center around the designing of a novel training strategy employing Curriculum Learning (CL), which aims to replicate the gradual learning process observed in human cognition, training on simpler tasks at the beginning and tackling more complex tasks as the training evolves. In order to evaluate the complexity of each sample image, we propose a novel indicator taking into account both the number of people and their distribution within the image. The experimentation phase encompassed 18 publicly available datasets; the obtained results validate the effectiveness of the proposed approach, surpassing the baseline state-of-the-art point detection by 71% and 70% in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), respectively. © The Author(s) 2024. |
| Author Keywords | Curriculum Learning; High density crowd counting; Point detection |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
41571  | 0.874 | Tomar A.; Verma K.K.; Kumar P. | People Counting Via Supervised Learning-Based 2D Cnn-Lr Model In Complex Crowd Images | Lecture Notes in Electrical Engineering, 1231 LNEE (2024) |
| 16111 | 0.869 | Ilyas N.; Shahzad A.; Kim K. | Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, And Performance Evaluation | Sensors (Switzerland), 20, 1 (2020) |
| 16692 | 0.867 | Savner S.S.; Kanhangad V. | Crowdnext: Boosting Weakly Supervised Crowd Counting With Dual-Path Feature Aggregation And A Robust Loss Function | IEEE Transactions on Instrumentation and Measurement, 74 (2025) |
| 16659 | 0.856 | Bakhtiarnia A.; Zhang Q.; Iosifidis A. | Crowd Counting On Heavily Compressed Images With Curriculum Pre-Training | 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 (2023) |
| 4875 | 0.853 | Avvenuti M.; Bongiovanni M.; Ciampi L.; Falchi F.; Gennaro C.; Messina N. | A Spatio- Temporal Attentive Network For Video-Based Crowd Counting | Proceedings - IEEE Symposium on Computers and Communications, 2022-June (2022) |
| 38647 | 0.853 | Guo X.; Gao M.; Zhai W.; Li Q.; Pan J.; Zou G. | Multiscale Aggregation Network Via Smooth Inverse Map For Crowd Counting | Multimedia Tools and Applications, 83, 22 (2024) |
| 16658 | 0.853 | Guo X.; Song K.; Gao M.; Zhai W.; Li Q.; Jeon G. | Crowd Counting In Smart City Via Lightweight Ghost Attention Pyramid Network | Future Generation Computer Systems, 147 (2023) |
| 50724 | 0.851 | De Cock L.; Verstockt S.; Vandeviver C.; Van de Weghe N. | Smart Crowd Management: The Data, The Users And The Solution | Leibniz International Proceedings in Informatics, LIPIcs, 240 (2022) |
| 16685 | 0.85 | Hu Y.; Liu Y.; Cao G.; Wang J. | Crowdcl: Unsupervised Crowd Counting Network Via Contrastive Learning | IEEE Internet of Things Journal, 12, 12 (2025) |