Abstract
Large mass gatherings such as pilgrimages, protests, etc., often pose serious challenges for the crowd management personnel to maintain public safety and security especially in dense crowds. These challenges can be mitigated through estimating the number of attendees as well as localizing them in a particular crowded event, where existing research studies are yet to provide accurate information in an efficient manner. Therefore, in this paper, we propose a novel deep learning architecture namely LC-Net to precisely and efficiently locate as well as count the attendees in dense crowds using a crowd localization map. Here, we exploit the notions of residual layers and dilated convolution to improve both the accuracy and efficiency of our architecture. Besides, we propose a new data augmentation technique to resize the high-resolution training images based on crowd density that substantially boosts our localization accuracy. Rigorous experimental evaluation of our proposed LC-Net over four different public crowd datasets such as NWPU-Crowd, UCF-QNRF, ShanghaiTech-A, and ShanghaiTech-B shows a substantial performance improvement while using LC-Net in terms of precision and recall in most of the cases. The improvement eventually results in an improved F1 score in all cases compared to the state-of-the-art approaches. Further, we present a real implementation of our proposed approach using a client–server application. In the server, we execute the LC-Net model over the images captured in real-time using an IP Camera and then visualize the results in a graphical manner. This implementation demonstrates the applicability of our proposed approach in real cases.
•Dilated residual network can localize people in a crowd image efficiently•High-definition images having sparse crowd may affect localization accuracy•Density-based image resizing is a novel data augmentation technique•Post processing layers can boost crowd localization accuracy