Statistical and entropy based abnormal motion detection

As visual surveillance systems are gaining wider usage in a variety of fields, they need to be embedded with the capability to interpret scenes automatically, which is known as human motion analysis (HMA). However, existing HMA methods are too domain specific and computationally expensive. This paper proposes a general purpose HMA method. It is based on the idea that human beings tend to exhibit random motion patterns during abnormal situations. Hence, angular and linear displacements of limb movements are characterized using basic statistical quantities. In addition, it is enhanced with the entropy of the Fourier spectrum to measure the randomness of the abnormal behavior. Various experiments have been conducted and prove that the proposed method has very high classification accuracy in identifying anomalous behavior.