Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.

Context Based Adoption of Ranking and Indexing Measures for Cricket Team Ranks

There is an international cricket governing body that ranks the expertise of all the cricket playing nations,known as the International Cricket Council(ICC).The ranking system followed by the ICC relies on the winnings and defeats of the teams.The model used by the ICC to implement rankings is deficient in certain key respects.It ignores key factors like winning margin and strength of the opposition.Various measures of the ranking concept are presented in this research.The proposed methods adopt the concepts of h-Index and PageRank for presenting more comprehensive ranking metrics.The proposed approaches not only rank the teams on their losing/winning stats but also take into consideration the margin of winning and the quality of the opposition.Three cricket team ranking techniques are presented i.e.,(1)Cricket Team-Index(ct-index),(2)Cricket Team Rank(CTR)and(3)Weighted Cricket Team Rank(WCTR).The proposed metrics are validated through the collection of cricket dataset,extracted from Cricinfo,having instances for all the three formats of the game i.e.,T20 International(T20i),One Day International(ODI)and Test matches.The comparative analysis between the proposed and existing techniques,for all the three formats,is presented as well.