Activity Recognition is a computer software program or a hardware device that can recognize the human activity being performed. With the rise of Machine learning and its applications everywhere, one of them is computer vision which uses Deep Learning Techniques. Program that utilizes these Machine or Deep learning techniques to recognize human activities is referred to as Human Activity Recognition System. The basic principle behind the action recognition is using sensors data, video stream or images to recognize the activity using neural networks. Modern human activity recognition systems are mainly trained and used upon video stream and images data that understand the features and actions variations in the data having similar or related movements. Human Activity Recognition plays a significant role in human-to-human and human-computer interaction. Manually driven system are highly time consuming and costlier. In this project, we aim at designing a cost-effective and faster Human Activity Recognition System which can process both video and image in order to recognize the activity being performed in it, thereby aiding the end user in various applications like surveillance, aiding purpose etc. This system will not only be cost effective but also as a utility-based system that can be incorporated in a large number of applications that will save time and aid in various activities that require recognition process, and save a lot of time with good accuracy Also, it will aid the blind people in knowing what's happening around them by incorporating this system into a handheld device.
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