Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors

TL;DR

This paper introduces a hierarchical method for multi-action recognition in videos that combines Fisher vectors and probabilistic integration to improve accuracy and efficiency in joint classification and segmentation.

Contribution

It presents a novel hierarchical approach that integrates class probabilities from Fisher vectors over overlapping temporal windows for joint action recognition and segmentation.

Findings

Achieves 85.0% accuracy on s-KTH dataset, outperforming recent methods.

Attains 40.9% accuracy on CMU-MMAC, surpassing baseline approaches.

System is approximately 40 times faster than GMM-based methods.

Abstract

We propose a hierarchical approach to multi-action recognition that performs joint classification and segmentation. A given video (containing several consecutive actions) is processed via a sequence of overlapping temporal windows. Each frame in a temporal window is represented through selective low-level spatio-temporal features which efficiently capture relevant local dynamics. Features from each window are represented as a Fisher vector, which captures first and second order statistics. Instead of directly classifying each Fisher vector, it is converted into a vector of class probabilities. The final classification decision for each frame is then obtained by integrating the class probabilities at the frame level, which exploits the overlapping of the temporal windows. Experiments were performed on two datasets: s-KTH (a stitched version of the KTH dataset to simulate multi-actions), and the challenging CMU-MMAC dataset. On s-KTH, the proposed approach achieves an accuracy of 85.0%, significantly outperforming two recent approaches based on GMMs and HMMs which obtained 78.3% and 71.2%, respectively. On CMU-MMAC, the proposed approach achieves an accuracy of 40.9%, outperforming the GMM and HMM approaches which obtained 33.7% and 38.4%, respectively. Furthermore, the proposed system is on average 40 times faster than the GMM based approach.