A survey of video datasets for human action and activity recognition
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TLDR
The survey introduced in this paper tries to cover the lack of a complete description of the most important public datasets for video-based human activity and action recognition and to guide researchers in the election of themost suitable dataset for benchmarking their algorithms.About:
This article is published in Computer Vision and Image Understanding.The article was published on 2013-06-01 and is currently open access. It has received 411 citations till now. The article focuses on the topics: Activity recognition.read more
Citations
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Proceedings ArticleDOI
The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities
TL;DR: The HTK toolkit is evaluated, a state-of-the-art speech recognition engine, in combination with multiple video feature descriptors, for both the recognition of cooking activities as well as the semantic parsing of videos into action units.
Journal ArticleDOI
A Review of Human Activity Recognition Methods
TL;DR: This work proposes a categorization of human activity methodologies and divides human activity classification methods into two large categories according to whether they use data from different modalities or not, and examines the requirements for an ideal human activity recognition dataset.
Journal ArticleDOI
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
TL;DR: A new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection is presented and shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used.
Proceedings ArticleDOI
Dynamic-MUSIC: accurate device-free indoor localization
TL;DR: MaTrack proposes a novel Dynamic-MUSIC method to detect the subtle reflection signals from human body and further differentiate them from those reflected signals from static objects to identify the human target's angle for localization.
Journal ArticleDOI
RGB-D-based action recognition datasets
TL;DR: In this article, a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view, 10 multi-view and 7 multi-person datasets, is presented.
References
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Proceedings ArticleDOI
Learning realistic human actions from movies
TL;DR: A new method for video classification that builds upon and extends several recent ideas including local space-time features,space-time pyramids and multi-channel non-linear SVMs is presented and shown to improve state-of-the-art results on the standard KTH action dataset.
Running experiments on Amazon Mechanical Turk
TL;DR: The authors presented new demographic data about the Mechanical Turk subject population, reviewed the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and compared the magnitude of effects obtained using Mechanical Turk and traditional subject pools.
Proceedings ArticleDOI
HMDB: A large video database for human motion recognition
TL;DR: This paper uses the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube, to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions.
Proceedings ArticleDOI
Recognizing human actions: a local SVM approach
TL;DR: This paper construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition and presents the presented results of action recognition.
Posted Content
Running experiments on Amazon Mechanical Turk
TL;DR: The authors presented new demographic data about the Mechanical Turk subject population, reviewed the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and compared the magnitude of effects obtained using Mechanical Turk and traditional subject pools.