Scale-aware CNN for crowd density estimation and crowd behavior analysis. (March 2023)

• Record Type: Journal Article
• Authors: Sharma, Vipul; Mir, Roohie Naaz; Singh, Chandrapal
• Is Part Of: Computers & electrical engineering. Volume 106(2023)
• Publication Date: 2023-03
• Subjects: Crowd density estimation -- Crowd behavior analysis -- CNN -- Visual surveillance -- Computer vision; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108569
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Visual surveillance is a highly demanded system in various real-time applications for civil and military sectors. For huge gatherings such as rallies and stadiums, crowd monitoring becomes a tedious task. Moreover, crowd density estimation also plays an important role to improve the performance of crowd monitoring system. Several techniques have been presented for crowd counting or density estimation and crowd behavior analysis. The crowd scenes contain noise, occlusion, cluttered environment which creates complexity to analyze the crowd behavior. To overcome the challenges of crowd monitoring, we present a combined solution to perform the crowd density estimation and crowd behavior analysis. The crowd density estimation is carried out by developing a new CNN based architecture which considers scale information and overcome the issues of scale variations. In order to deal with this issue of scale variation, we introduce a scale-aware attention module where multiple branches of self-attention module are concatenated to improve the scale variation realization. Further, we present the crowd behavior analysis model which uses motion map and energy level distribution based features to detect the abnormal crowd behavior. The Proposed approach achieves average accuracy of abnormal event detection as 98.95, and crowd counting accuracy as 97.60. Furthermore, the average MAE and MSE is achieved as 58.60, 98.55 for ShanghaiTech Part A dataset and 7.55, 8.50 for Part B dataset, respectively. Similarly, the MAE and MSE for UCF 50 dataset is obtained as 210.20, and 260.80, respectively which shows a significant improvement over other techniques discussed in the experimental study. Highlights: Presented a joint solution for crowd density estimation and crowd behavior analysis. CNN model considers scale information and overcome the issues of scale variations. Behavior analysis model which uses motion map and energy level distribution. Relative study to highlight that proposed approach achieves better performance.