Role Identification from Human Activity Videos using Recurrent Neural Networks

Recognizing the underlying roles in mutual activity is more informative. Role identification has the potential to improve wide range of applications, of activity recognition from safety and security to healthcare. In recent research, for role identification, work is done to identify roles by capturing the knowledge of body parts from an image. This work is complex and not sufficient to take input as English sentences and capture the sequencing and relationship between words. There is a need for simple work which could use recent technologies like Recurrent Neural Networks to capture the recurrent nature of sentences to identify roles. The contribution of this work is proposing a Computational Long Term Memory model where sentences are stored as features and given to a Recurrent Neural Network to identify roles. The appropriate dataset is not available for role identification using sentences. In this view, this work attempt to develop a new custom dataset. The proposed model is tested on accuracy using various Recurrent Neural Networks like Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) etc. The LSTM model gave effective accuracy of 60% on the small custom dataset.