Fast crowd density estimation with convolutional neural networks. (August 2015)

• Record Type: Journal Article
• Authors: Fu, Min; Xu, Pei; Li, Xudong; Liu, Qihe; Ye, Mao; Zhu, Ce
• Is Part Of: Engineering applications of artificial intelligence. Volume 43(2015:Jul.)
• Page Start: 81
• Page End: 88
• Publication Date: 2015-08
• Subjects: Crowd density -- Convolutional neural network -- Neural networks; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.04.006
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: As an effective way for crowd control and management, crowd density estimation is an important research topic in artificial intelligence applications. Since the existing methods are hard to satisfy the accuracy and speed requirements of engineering applications, we propose to estimate crowd density by an optimized convolutional neural network (ConvNet). The contributions are twofold: first, convolutional neural network is first introduced for crowd density estimation. The estimation speed is significantly accelerated by removing some network connections according to the observation of the existence of similar feature maps. Second, a cascade of two ConvNet classifier has been designed, which improves both of the accuracy and speed. The method is tested on three data sets: PETS_2009, a Subway image sequence and a ground truth image sequence. Experiments confirm the good performance of the method on the same data sets compared with the state of the art works.