Multi class activity classification in videos using Motion History Image generation

TL;DR

This paper explores using Motion History Images to classify multiple human activities in videos, demonstrating its effectiveness and analyzing limitations for security and entertainment applications.

Contribution

It introduces a method to generate MHIs for multi-activity classification and evaluates its performance across six different actions.

Findings

MHI effectively captures temporal activity information.

Classifier achieves promising accuracy on multi-activity videos.

Identifies scenarios where MHI struggles to accurately represent actions.

Abstract

Human action recognition has been a topic of interest across multiple fields ranging from security to entertainment systems. Tracking the motion and identifying the action being performed on a real time basis is necessary for critical security systems. In entertainment, especially gaming, the need for immediate responses for actions and gestures are paramount for the success of that system. We show that Motion History image has been a well established framework to capture the temporal and activity information in multi dimensional detail enabling various usecases including classification. We utilize MHI to produce sample data to train a classifier and demonstrate its effectiveness for action classification across six different activities in a single multi-action video. We analyze the classifier performance and identify usecases where MHI struggles to generate the appropriate activity image and discuss mechanisms and future work to overcome those limitations.