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Recognizing human actions: a local SVM approach
TLDR
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. Expand
Learning realistic human actions from movies
TLDR
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. Expand
Space-time interest points
TLDR
This work builds on the idea of the Harris and Forstner interest point operators and detects local structures in space-time where the image values have significant local variations in both space and time to detect spatio-temporal events. Expand
On Space-Time Interest Points
  • I. Laptev
  • Mathematics, Computer Science
  • International Journal of Computer Vision
  • 1 September 2005
TLDR
This paper builds on the idea of the Harris and Förstner interest point operators and detects local structures in space-time where the image values have significant local variations in both space and time and illustrates how a video representation in terms of local space- time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds. Expand
Evaluation of Local Spatio-temporal Features for Action Recognition
TLDR
It is demonstrated that regular sampling of space-time features consistently outperforms all testedspace-time interest point detectors for human actions in realistic settings and is a consistent ranking for the majority of methods over different datasets. Expand
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
TLDR
This work proposes a novel Hollywood in Homes approach to collect data, collecting a new dataset, Charades, with hundreds of people recording videos in their own homes, acting out casual everyday activities, and evaluates and provides baseline results for several tasks including action recognition and automatic description generation. Expand
Actions in context
TLDR
This paper automatically discover relevant scene classes and their correlation with human actions, and shows how to learn selected scene classes from video without manual supervision and develops a joint framework for action and scene recognition and demonstrates improved recognition of both in natural video. Expand
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
TLDR
This work designs a method to reuse layers trained on the ImageNet dataset to compute mid-level image representation for images in the PASCAL VOC dataset, and shows that despite differences in image statistics and tasks in the two datasets, the transferred representation leads to significantly improved results for object and action classification. Expand
Is object localization for free? - Weakly-supervised learning with convolutional neural networks
TLDR
A weakly supervised convolutional neural network is described for object classification that relies only on image-level labels, yet can learn from cluttered scenes containing multiple objects. Expand
Actions in context
TLDR
This paper automatically discover relevant scene classes and their correlation with human actions, and shows how to learn selected scene classes from video without manual supervision and develops a joint framework for action and scene recognition and demonstrates improved recognition of both in natural video. Expand
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