Human actions recognition in video scenes from multiple camera viewpoints

Abstract Internet of Video Things (IoVT) has been proposed and studied as a scenario where video cameras are ubiquitous and continuously acquiring data from their surroundings. In order to enable handling of a large amount of data generated in IoVT architectures, robust autonomous video processing must be performed. An important application is the recognition of different actions performed by humans in the context of security. This research evolves a previously published work, by reducing the input dimensionality to the recognition system, making it more robust to variations in the position of the body in each video frame, and by using a Multilayer Perceptron Artificial Neural Network whose hyperparameters are here optimized by a Genetic Algorithm. Significant improvements in the recognition rate have been obtained, despite the use of a more straightforward pre-processing phase and the increase in the number of viewpoints from the video cameras.