Two‐layer discriminative model for human activity recognition. Issue 4 (19th April 2016)

• Record Type: Journal Article
• Authors: Selmi, Mouna; El‐Yacoubi, Mounîm A.; Dorizzi, Bernadette
• Is Part Of: IET computer vision. Volume 10:Issue 4(2016)
• Page Start: 273
• Page End: 279
• Publication Date: 2016-04-19
• Subjects: image classification -- feature extraction -- spatiotemporal phenomena -- image segmentation -- video streaming -- support vector machines -- probability -- stochastic processes -- image sequences; two-layer discriminative model -- human activity recognition -- action recognition -- static classifiers -- local interest point features -- IP features -- spatiotemporal IP -- sliding window segmentation technique -- video stream -- local bag of words -- stochastic vector sequence -- conjoint segmentation -- action sequence recognition -- continuous video stream -- first-layer support vector machine; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2015.0235
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Most of recent methods for action/activity recognition, usually based on static classifiers, have achieved improvements by integrating context of local interest point (IP) features such as spatiotemporal IPs by characterising their neighbourhood under different scales. In this study, the authors propose a new approach that explicitly models the sequential aspect of activities. First, a sliding window segmentation technique splits the video stream into overlapping short segments. Each window is characterised by a local bag of words of IPs encoded by motion information. A first‐layer support vector machine provides for each window a vector of conditional class probabilities that summarises all discriminant information that is relevant for sequence recognition. The sequence of these stochastic vectors is then fed to a hidden conditional random field for inference at the sequence level. They also show how their approach can be naturally extended to the problem of conjoint segmentation and recognition of a sequence of action classes within a continuous video stream. They have tested their model on various human action and activity datasets and the obtained results compare favourably with current state of the art.