01912nas a2200241   4500000000100000000000100001008004100002260001200043653002500055653001500080653001800095653003900113100002100152700002100173700001700194700001900211245005800230856008100288300000700369490000600376520127400382022001401656       2020                            d  c06/202010aActivity recognition10aMonitoring10aRandom Forest10aConvolutional Neural Network (CNN)1 aKamal Kant Verma1 aBrij Mohan Singh1 aH L Mandoria1 aPrachi Chauhan00aTwo-Stage Human Activity Recognition Using 2D-ConvNet  uhttps://www.ijimai.org/journal/sites/default/files/2020-05/ijimai_6_2_13.pdf  a110 v63 aThere is huge requirement of continuous intelligent monitoring system for human activity recognition in various domains like public places, automated teller machines or healthcare sector. Increasing demand of automatic recognition of human activity in these sectors and need to reduce the cost involved in manual surveillance have motivated the research community towards deep learning techniques so that a smart monitoring system for recognition of human activities can be designed and developed. Because of low cost, high resolution and ease of availability of surveillance cameras, the authors developed a new two-stage intelligent framework for detection and recognition of human activity types inside the premises. This paper, introduces a novel framework to recognize single-limb and multi-limb human activities using a Convolution Neural Network. In the first phase single-limb and multi-limb activities are separated. Next, these separated single and multi-limb activities have been recognized using sequence-classification. For training and validation of our framework we have used the UTKinect-Action Dataset having 199 actions sequences performed by 10 users. We have achieved an overall accuracy of 97.88% in real-time recognition of the activity sequences.  a1989-1660