Human Tracking and Activity Recognition for Surveillence Applications

Abstract

Tracking and study of behavioural changes of human beings through vision is a challenging task. For surveillance, automated systems are important which can observe the traffic and can detect the abnormality. For tracking human or any kind of object, colour feature based mean shift technique is widely used. This technique uses Bhattacharya coefficient to locate the object based on the maximisation of the similarity function between object model and candidate model. Traditional mean shift algorithm fails when the object having large motion, occlusion, corrupted frames etc. In addition to that, the technique is not automatic to initiate the tracking. To overcome all these problems, this thesis work proposed a technique which uses three additional modules to the traditional method to make it more efficient. The proposed modules used human detection by modelling through star skeletonization, followed by block search algorithm and occlusion handling. Block search algorithm helped to supply an overlapping area to candidate model to continue the track when tracking fails due to fast motion. Occlusion handling helped in initiating the tracking after prolonged period of occlusion. The proposed method has been tested on real time data and it outperforms the conventional method effectively to overcome the mentioned problems up to large extent. Human activity recognition is a hierarchical procedure which confirms abnormality step by step. Low level activity recognition is a trajectory based application in which trajectory of tracks of a human being helps to detect the abnormal events like person fell down, illegal entry, abnormal loitering, line formation etc. At high level, human pose will be detected by the help of shape based human pose detection. The main aim of the system is to make a person independent real-time human activity recognition with decreased false alarm rates.