Translation domain segmentation model based on improved cosine similarity for crowd motion segmentation

Abstract

With the continuous growth of the global population, large-scale public gatherings have become more common, and crowd management at these gatherings has become an urgent problem for public safety management. Crowd motion analysis and early warning based on crowd motion segmentation in video surveillance systems has become an important research topic in computer vision. A translation domain segmentation (TDS) model based on improved cosine similarity (ICS) is proposed to segment moving crowds with different crowding levels and complex motion modes. The method reconstructs the objective function of the basic TDS model by ICS to simultaneously measure the difference between both magnitude and direction of two vectors; thus, undersegmentation due to the magnitude difference can be avoided. By switching between "localization" and "globalization" modes of the objective function, the algorithm can be applied to segment crowds with different densities and motion states. Moreover, by simultaneously introducing local motion magnitude and local frame difference magnitude thresholds, nonforeground regions can be excluded from the initial regions during region evolution. Experimental results show that the proposed method achieves superior performance and higher accuracy compared to existing flow field-based methods when applied to complex scenes containing moving crowds.

Video-based Domain Generalization for Abnormal Event and Behavior Detection

“…Cosine resemblance (CS) quantifies the resemblance of video frames based on the cosine value of the frames. This study estimated the CS value for all input frames using equation ( 1) to establish the suitable similarity threshold [27].…”

Panic Detection in Crowded Scenes

“…The methods introduced in [12,13] extract frames from video CCTV cameras. Some researchers introduced unified frameworks for crowd congestion analysis, individual counting, crowd flow analysis on pedestrian pathways, crowd fight analysis, panic and bottleneck detection during exit and entrance [14][15][16]. In [17], a hybrid method was used to analyze crowd from different angles, such as crowd moving in different directions, computing statistics related to a static crowd, calculation of areas covered by potential crowd, and crowd's social behavior analysis in the form of groups or as a whole.…”