Crowd Analysis for Congestion Control Early Warning System on Foot Over Bridge

Crowds occur in a variety of situations like concerts, rallies, marathons, stadiums, railway stations, etc. Crowd analysis is essential from the point of view of safety and surveillance, abnormal behavior detection and thereby reducing the chance of a mishap. Generally, congestion in the crowd can lead to severe problems like a stampede. This congestion is due to increasing crowd count; thereby increasing the crowd density in regions and abnormal crowd motion. Most of the congestion control approaches follow a hardware-oriented approach. This paper proposes a software-oriented approach, Congestion Control Early Warning System (CCEWS), for congestion control with the help of object detection and object tracking technique. Object detection is performed by following the faster R-CNN architecture in which Google inception model is used as a pre-trained CNN model and with the help of proposed object tracking technique the crowd abnormality is analyzed. The proposed congestion control technique exhibits quite significant results on the proposed dataset made from the virtual simulation of FOB (foot over bridge) scenario.