Zeeshan Rasool Lodhi2019-01-312019-01-312018https://escholar.umt.edu.pk/handle/123456789/3635Dr. Malik Tahir HassanWith the increase in the population of elderly, especially in the developing countries, the increase in the number of aged people living independently has arose a great risk. It has led to increased health care financial cost which can become a great burden on our society. The falls of elderly has become a major cause for serious injuries. Due to this risk many studies have been done on human posture recognition, its different approaches and how we can predict the fall before any serious injury occurs to the elderly people. This work comprises of three parts. Firstly, this work provides an extensive survey on human posture recognition systems with respect to eras, different approaches used in previous researches and performance evaluation of different datasets used for human posture recognition. Secondly, in this work a new approach for human posture recognition is proposed which uses Radon Transform as a feature extractor to extract projection feature in an image frame. The feature extracted using radon transform will be used to train the machine learning classifier for postures classification. Thirdly, classifier boosting is performed on the machine learning classifiers to increase the overall accuracy of the proposed approach. Total five postures are recognized using this approach which are walking, sitting, lying on the sofa, lying on the ground and crouching. This thesis also provides the performance comparison between a recent existing approach and the proposed approach. The proposed algorithm has achieved an accuracy of 98.7% on MCF (multiple cameras fall) dataset.enGaussian Mixture Model, Karmen Loeve, Markov Random FieldMSThesis