HARTO: Human Activity Recognition Through Optical Flow Architecture
Luis-Jesus Marhuenda, Francisco Gomez‐Donoso, Miguel Cazorla · 2024
Recognition of human actions is a very important task in computer vision research. It can be used for robots, to better understand human companions; by surveillance systems, to detect suspicious action; or even in health care setups to detect falls or other harming events. Despite the fact that action recognition is a recurrent topic, it still poses a significant challenge. In this work, we present HARTO, a novel optical flow-based deep learning architecture for classifying human action. The input of the network is an array of optical flow frames extracted from a video. This intermediate representation allows us to optimally encode the motion present in videos to provide accurate predictions. We trained and tested our approach with the Toyota Smarthome Dataset, which is a commonly used benchmark for this task. We follow the protocols proposed by the creators of the dataset and ranked #3 on a public benchmark at the time of writing.