With the development of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are not sufficient. Due to the limitation of these devices the useable command set is also limited. Direct use of hands can be used as an input device for providing natural interaction.
Human gestures have long been an important way of communication, adding emphasis to voice messages or even being a complete message by itself. Such human gestures could be used to improve human machine interface. These may be used to control a wide variety of devices remotely. Vision-based framework can be developed to allow the users to interact with computers through human gestures.
Sequence prediction problems have been around for a long time. They are considered as one of the hardest problems to solve in the data science industry. These include a wide range of problems; from predicting sales to finding patterns in stock markets’ data, from understanding movie plots to recognizing your way of speech, from language translations to predicting your next word on your iPhone’s keyboard.
With the recent breakthroughs that have been happening in data science, it is found that for almost all of these sequence prediction problems, Long short Term Memory networks, a.k.a LSTMs have been observed as the most effective solution.
LSTMs have an edge over conventional feed-forward neural networks and RNN in many ways. This is because of their property of selectively remembering patterns for long durations of time.For this purpose LSTM are used in this project.
- Real time detection
- Less Memory consumption
- Better Performance than traditional CNN
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