**2. Related work**

The research trend that was primarily focused on detection of objects, visual object tracking and human body part detection, has advanced to pose estimation recently. Various visual tracking architectures have been proposed such as those based on convolutional neural networks and particle filtering and colored area tracking using mean shift tracking through temporal image sequence [12]. A survey of approaches for intruder detection systems in a camera monitored frame for surveillance was explained by C. Savitha and D. Ramesh [13]. A. Shahbaz and K. Jo also proposed a human verifier which is a SVM classifier based on histogram of oriented gradients along with an algorithm for change detection based on Gaussian mixture model [14]. But still there was a need of more precise detection algorithm that would accurately predict minor features as well. Human and Object detection evolved to detection of human body parts. L. Kong, X. Yuan and A.M.Maharajan introduced framework for automated joint detection using depth frames [15]. A cascade of Deep neural networks was used for Pose Estimation formulated as a joint regression problem and cast in DNN [16]. A full image and 7-layered generic convolutional DNN is taken as input to regress the location of each body joint. In [17], long-term temporal coherence was propagated to each stage of the overall video and data of joint position of initial posture was generated. A multi-
