Abstract
Congestion typically occurs when the number of crowds exceeds the capacity of facilities. In some cases, when buildings have to be evacuated, people might be trapped in congestion and cannot escape from the building early enough which might even lead to stampedes. Crowd Congestion Mapping (CCM) is a system that enables organizations to find information about the crowd congestion in target places. This project provides the ability to make the right decision to determine the reasons that led to that and to do the appropriate procedures to avoid this from happening again by optimizing locations and dimensions of the emergency exits less congested path on the target places. The system collects crowd congestion data from the locations and makes it available to corporations via target map. The congestion is plotted on target place map, for example, the red line for highly congested location, the pink line for mildly congested location and green line for free flow of humans in the location.
Highlights
There is great interest in the surveillance system, especially in cases of high congestion as the ability to identify objects and follow them or to detect the times that become congestion
A system was proposed for the identification of cases of crowded gatherings that may occur at certain times, which may lead to security problems, interruptions or other problems.this system is based on image feature extraction and Feed-Forward Neural Networks (FFNN)
Crowd counting mapping approach was proposed, which based on image feature extraction and FFNN
Summary
There is great interest in the surveillance system, especially in cases of high congestion as the ability to identify objects and follow them or to detect the times that become congestion. A system was proposed for the identification of cases of crowded gatherings that may occur at certain times, which may lead to security problems, interruptions or other problems.this system is based on image feature extraction and Feed-Forward Neural Networks (FFNN). This system plotted gathered information on the map to explore the hazardous locations to take a decision in this situation
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More From: International Journal of Advanced Computer Science and Applications
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