Learning Latent Temporal Manifolds for Recognition and Prediction of Multiple Actions in Streaming Videos using Deep Networks
Binu M. Nair · OhioLink ETD Center (Ohio Library and Information Network) · 2015
Recognizing multiple types of actions appearing in a continuous temporal order from a streaming video is the key to many possible applications ranging from real-time surveillance to egocentric motion for human computer interaction.Current state of the art algorithms are more focused either on holistic video representation or on finding a specific activity in video sequences.But the major drawback is that these algorithms work only on applications pertaining to unconstrained video search from the web and requires the complete sequence for reporting what kind of actions are present.In this dissertation, we propose an algorithm to detect and recognize multiple actions in a streaming sequence at every instant.This approach was successful in recognizing the type of action being performed and also provides a percentage of completion of that action at every instant in realtime.This system is invariant to the number of frames and the speed at which the action is being performed.Apart from these benefits, the proposed model can also predict the motion descriptors at future instances corresponding to the action present.Since human motion is inherently continuous in nature, the algorithm presented in this dissertation computes novel motion descriptors based iii Thank you Dr. Vijayan K. Asari