Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition using visual phrases
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Discriminative Latent Models for Recognizing Contextual Group Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sum-product networks for modeling activities with stochastic structure
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
A chains model for localizing participants of group activities in videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper, we propose an activity localization method with contextual information of person relationships. Activity localization is a task to determine "who participates to an activity group", such as detecting "walking in a group" or "talking in a group". Usage of contextual information has been providing promising results in the previous activity recognition methods, however, the contextual information has been limited to the local information extracted from one person or only two people relationship. We propose a new context descriptor named "contextual spatial pyramid model (CSPM)", which represents the global relationships extracted from the whole of activities in single images. CSPM encodes useful relationships for activity localization, such as "facing each other". The experimental result shows CSPM improve activity localization performance, therefore CSPM provides strong contextual cues for activity recognition in complex scenes.