Human activity detection and action recognition in videos using convolutional neural networks
Human activity detection and action recognition in videos using convolutional neural networks
The main aim of this work is to detect and track human activity, and classify actions for two publicly available video databases. In this work, a novel approach of feature extraction from video sequence by combining Scale Invariant Feature Transform and optical flow computation are used where shape, gradient and orientation features are also incorporated for robust feature formulation.