Skeleton based View Invariant Human Action Recognition using Convolutional Neural Networks

2019 International journal of recent technology and engineering  
The skeletal based human action recognition has its significant applications in the field of human computer interaction and human recognition from surveillance videos. However, the tasks suffers from the major challenges like view variance and noise in the data. These problems are limiting the performance of human action recognition. This paper focuses to solve these problems by adopting sequence based view invariant transform to effectively represent the spatio-temporal information of the
more » ... tal data. The task of human action recognition in this paper is performed in three stages. Firstly, the raw 3D skeletal joint data obtained from the Microsoft Kinect sensor is transformed to eliminate the problem of view variations on a spatio-temporal data by implementing sequence based view invariant transform. In the second stage, the transformed joint locations of the skeletal data will be converted to RGB images by a color coding technique and forms a transformed joint location maps (TJLMs) . As a third stage, the discriminating features were extracted by the novel CNN architecture to performs the human action recognition task by means of class scores. Noticeable amount of recognition scores are achieved. Extensive experiments in four difficult 3D action datasets constantly show our method's superiority. The performance of the proposed method is compared with the other state-of-the-art methods. He is the chair of the Biomechics and vision computing research center. His works focus on mechine learing, biomechanics, artificial intelligence, human motion analysis and sign language machine translation. His research explores how motion capture data models can effectively model low end video objects in real time for better recogntion and analysis. He is particularly intersted in developing new innovations in the areas of computer vision and mechine learing. He has authored several publications in these fields. Dr. O. Srinivasa Rao did B.Tech, M.Tech and obtained Ph.D in CSE from JNTUK, KAKINADA. His Ph.D specialization is cryptography and Network security. He presented more than 60 research papers in various International journals and two research papers in National conferences, one research paper in international conference. He had more than 20 years of teaching experience and he was former Head of CSE at University College of Engineering vizianagaram, JNTUK and currently working as Professor of CSE at University College of Engineering, JNTUK, Kakinada. He guided one Ph.D and more than 90 M.Tech and MCA students' projects. Currently he is guiding 4 Ph.D and 8 M.Tech, 2 MCA students' projects. His fields of interest are Cryptography, Network security, Image Processing and Data Mining.
doi:10.35940/ijrte.b3547.078219 fatcat:xiyh5cn47zfn3nm776amdrjaye