Near Real-time Crowd Counting using Deep Learning Approach

Abstract

In the current digital era, at many places crowd counting mechanisms still rely on old-fashioned methods such as maintaining registers, making use of people counters and sensors based counting at entrance. These methods fail in the places where the movement of people is completely random, highly variable and dynamic. These methods are time consuming and tedious. The proposed system is developed for situations where emergency evacuations are required such as fire outbreaks, calamitous events, etc. and making informed decisions on the basis of the number of people such as food, water, detecting congestion, etc. A deep convolution neural network (DCNN) based system can be used for near real-time crowd counting. The system uses NVIDIA GPU processor to exploit the parallel computing framework to achieve swift and agile processing of the video feed taken through a camera. This work contributes towards constructing a model to detect heads captured by CCTV camera. The model is trained extensively by providing several scenarios such as overlapping heads, partial visibility of heads etc. This system provides significant accuracy in estimating the head count in dense population in reasonably less amount of time.