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Hand Gesture Based Quadcopter Control Using Image Processing And Adaptive Machine Learning

Accurately recognizing gesture in real time is a complex problem to solve. It can be addressed to some extent using colored gloves and markers, but models so built do not generalize well. Another option is to utilize boundary based gesture recognition, but these are more complex to build and prediction is time consuming. Through this work we try to resolve both these issues by construction of a straightforward machine learning architecture which accurately implements gesture recognition in real time and can also be utilized to build a generalized model. The solution we have developed is based on the premise that an architecture which can contextually recognize gestures and can also adaptively identify variations to the same can be used for solving problems ranging from body language detection, translating sign language to devising an intuitive method for controlling UAVs (Unmanned Aerial Vehicles).