Tasks of visual crowd analysis in intelligent video surveillance systems

Abstract

Modern urban population growth creates challenges for public safety, in particular due to crowds. This stimulates the development of new crowd management methods that require automated analysis. Visual crowd analysis based on computer vision technologies is a key tool for solving these problems. The development of deep learning has significantly improved the monitoring systems used for urban surveillance, social distancing control, transportation and event management. However, crowd analysis remains challenging due to occlusions, scale variations, unpredictable movement patterns, and complex behavior. To overcome these challenges, new algorithms, models, and large-scale datasets are needed to enable real-time analysis. The main tasks include people counting, object detection, motion analysis, behavior recognition, and anomaly detection. Deep neural networks and transfer learning significantly increase the accuracy and adaptability of such systems, which helps to improve public safety and manage the flow of people.