Segmentation Method to Obtain Required Area of Interest Using Deep Learning

Abstract

Segmentation is very important in the area of the analysis of the image or image in the videos. Crowd behavior analysis is very valuable economically and has numerous potential applications, including intelligent video surveillance, virtual reality, public safety, and other areas. In densely populated areas, humans might be regarded as a collection of several groups with similar motion characteristics. Groups are essential components of a crowd. A quick crowd segmentation strategy based on crowd spatiotemporal linkages is presented to get fundamental crowd interactions for subsequent crowd behavior analysis. The processing of medical images, face identification, pedestrian detection, and other applications make extensive use of image segmentation technologies. Segmentation is on Region-base, edge detection, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. are some of the current picture segmentation techniques. This approach takes into account both pedestrian spatial information and crowd motion data. The technique may be used on different crowd scenarios and can get the same segmentation results in less time. The effectiveness and efficiency of the suggested approach for crowd segmentation are shown by extensive experiment results on videos of real-world crowd scenarios.