We are hired by a smart tv manufacturing company which aims to add new gesture recognition capabilities to its next generation TVs which are set to be released in the next 1 year.
Background: Everywhere we look around us, we are able to see devices which are functioning more than they are supposed to. We have from everything from everyday household devices such as smart watches, smart bulbs, smart TVs to sophisticated machinery such as smart cars, smart medical devices. All of these things have one common theme to them, that is, they are able to reduce the manual effort required to do a particular task. Examples of this are voice recognition to activate your speakers and voice assistants like Siri and Cortana who are able to recognise the voice, decipher the meaning and then work on it. Taking one step further from this is the gesture recognition.
Problem Statement: We need to build a predictive model using advanced Deep Learning algorithms which will be able to predict from a list of 5 gestures and then work accordingly.
This case study wouldn’t have been possible without the help of my team mate Keerthi Gayam. Thanks Keerthi for your big help on this.
Please note that the main body of the code was provided to us by IIIT-B and UpGrad and we have worked only on the hyper-parameters addition and their tuning. The same has been highlighted in our codes.