NDLI: Two-layer discriminative model for human activity recognition

Content Provider	IET Digital Library
Author	Selmi, Mouna Yacoubi, Mounîm A. El Dorizzi, Bernadette
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.
Starting Page	273
Ending Page	279
Page Count	7
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 4, Jun (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0235
Journal	IET Computer Vision
Publisher Date	2016-03-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Action Recognition Action Sequence Recognition Computer Vision And Image Processing Technique Conjoint Segmentation Continuous Video Stream Feature Extraction First-layer Support Vector Machine Human Activity Recognition Image Classification Image Recognition Image Segmentation Image Sequence IP Feature Knowledge Engineering Technique Local Bag of Words Local Interest Point Feature Probability Sliding Window Segmentation Technique Spatiotemporal IP Spatiotemporal Phenomena Static Classifier Statistics Stochastic Linearised SCUC Stochastic Vector Sequence Support Vector Machine Two-layer Discriminative Model Video Signal Processing Video Stream Video Streaming
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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