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Human Action Monitering Using HoG of optical flow

Siddharth Sharma, Keshaw Kumar, Alex Noel Joseph Raj


Due to immence increase in the number of CCTV cameras it has become a requrement that computers should be able to automatically detect and analyse human actions in the video feed. This research paper is intended to make the serviellance system fully autonomous, an ultimate goal in the area of smart serveillence system research. In this paper a new methodlogy is presented for autonomous multiple human actions recognition using HoG of optical flow. Three features viz. HoG of Optical Flow, shape, and trajectory have been fused together to define the motion descriptor. This motion descriptor is used to train a feedforward neural network using backpropagation algorithm to classify the human actions in the video feed in real time. The method proves to be very effective in calssifying multiple behaviors with an overall efficency of 80.9%. Also the running time of the algorithm is small enough to be used in real time.

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