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Explicit Modeling of Human-Object Interactions in Realistic Videos
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Person Spotting: Video Shot Retrieval for Face Sets
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Semi-supervised On-Line Boosting for Robust Tracking
inferredcites →https://doi.org/10.1007/978-3-540-88682-2_49openalex-snapshot
Tracking with Dynamic Hidden-State Shape Models
inferredcites →https://doi.org/10.1007/978-3-642-15549-9_32openalex-snapshot
Dense Point Trajectories by GPU-Accelerated Large Displacement Optical Flow
inferredcites →https://doi.org/10.1007/978-3-642-15549-9_37openalex-snapshot
Representing Pairwise Spatial and Temporal Relations for Action Recognition
inferredcites →https://doi.org/10.1007/978-3-642-15549-9_39openalex-snapshot
Modeling the Temporal Extent of Actions
inferredcites →https://doi.org/10.1007/978-3-642-15552-9_29openalex-snapshot
Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification
inferredcites →https://doi.org/10.1007/978-3-642-35749-7_17openalex-snapshot
Human Focused Action Localization in Video
inferredcites →https://doi.org/10.1007/s11263-008-0142-8openalex-snapshot
Searching for Complex Human Activities with No Visual Examples
inferredcites →https://doi.org/10.1007/s11263-014-0733-5openalex-snapshot
The Pascal Visual Object Classes Challenge: A Retrospective
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Simultaneous tracking of multiple body parts of interacting persons
inferredcites →https://doi.org/10.1016/j.cviu.2010.11.012openalex-snapshot
Robust sequence alignment for actor–object interaction recognition: Discovering actor–object states
inferredcites →https://doi.org/10.1109/34.910878openalex-snapshot
The recognition of human movement using temporal templates
inferredcites →https://doi.org/10.1109/cvpr.2001.990935openalex-snapshot
Event-based analysis of video
inferredcites →https://doi.org/10.1109/cvpr.2005.139openalex-snapshot
Efficient Mean-Shift Tracking via a New Similarity Measure
inferredcites →https://doi.org/10.1109/cvpr.2005.177openalex-snapshot
Histograms of Oriented Gradients for Human Detection
inferredcites →https://doi.org/10.1109/cvpr.2005.58openalex-snapshot
Actions Sketch: A Novel Action Representation
inferredcites →https://doi.org/10.1109/cvpr.2008.4587468openalex-snapshot
Progressive search space reduction for human pose estimation
inferredcites →https://doi.org/10.1109/cvpr.2008.4587628openalex-snapshot
Action recognition with motion-appearance vocabulary forest
inferredcites →https://doi.org/10.1109/cvpr.2008.4587630openalex-snapshot
Classification using intersection kernel support vector machines is efficient
inferredcites →https://doi.org/10.1109/cvpr.2008.4587726openalex-snapshot
Recognizing primitive interactions by exploring actor-object states
inferredcites →https://doi.org/10.1109/cvpr.2008.4587727openalex-snapshot
Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition
inferredcites →https://doi.org/10.1109/cvpr.2008.4587735openalex-snapshot
Action recognition by learning mid-level motion features
inferredcites →https://doi.org/10.1109/cvpr.2008.4587756openalex-snapshot
Learning realistic human actions from movies
inferredcites →https://doi.org/10.1109/cvpr.2009.5206513openalex-snapshot
“Who are you?” - Learning person specific classifiers from video
inferredcites →https://doi.org/10.1109/cvpr.2009.5206744openalex-snapshot
Recognizing realistic actions from videos “in the wild”
inferredcites →https://doi.org/10.1109/cvpr.2010.5539882openalex-snapshot
Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes
inferredcites →https://doi.org/10.1109/cvpr.2010.5540231openalex-snapshot
P-N learning: Bootstrapping binary classifiers by structural constraints
inferredcites →https://doi.org/10.1109/cvpr.2010.5540234openalex-snapshot
Grouplet: A structured image representation for recognizing human and object interactions
inferredcites →https://doi.org/10.1109/cvpr.2010.5540235openalex-snapshot
Modeling mutual context of object and human pose in human-object interaction activities
inferredcites →https://doi.org/10.1109/cvpr.2011.5995646openalex-snapshot
Actom sequence models for efficient action detection
inferredcites →https://doi.org/10.1109/iccv.2003.1238420openalex-snapshot
Recognizing action at a distance
inferredcites →https://doi.org/10.1109/iccv.2007.4409105openalex-snapshot
Retrieving actions in movies
inferredcites →https://doi.org/10.1109/iccv.2009.5459154openalex-snapshot
Activity recognition using the velocity histories of tracked keypoints
inferredcites →https://doi.org/10.1109/iccv.2009.5459278openalex-snapshot
Robust tracking-by-detection using a detector confidence particle filter
inferredcites →https://doi.org/10.1109/iccv.2009.5459279openalex-snapshot
Automatic annotation of human actions in video
inferredcites →https://doi.org/10.1109/iccv.2009.5459368openalex-snapshot
Learning actions from the Web
inferredcites →https://doi.org/10.1109/icpr.2004.1334462openalex-snapshot
Recognizing human actions: a local SVM approach
inferredcites →https://doi.org/10.1109/tpami.2007.250600openalex-snapshot
Tracking People by Learning Their Appearance
inferredcites →https://doi.org/10.1109/tpami.2007.70711openalex-snapshot
Actions as Space-Time Shapes
inferredcites →https://doi.org/10.1109/tpami.2009.167openalex-snapshot
Object Detection with Discriminatively Trained Part-Based Models
inferredcites →https://doi.org/10.1109/tpami.2009.83openalex-snapshot
Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition
inferredcites →https://doi.org/10.1109/tpami.2010.143openalex-snapshot
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
inferredcites →https://doi.org/10.1109/tpami.2011.158openalex-snapshot
Weakly Supervised Learning of Interactions between Humans and Objects
inferredcites →https://doi.org/10.1109/vspets.2005.1570899openalex-snapshot
Behavior Recognition via Sparse Spatio-Temporal Features
inferredcites →https://doi.org/10.5244/c.23.90openalex-snapshot
Exemplar-based Action Recognition in Video
inferredcites →https://doi.org/10.5244/c.24.50openalex-snapshot
High Five: Recognising human interactions in TV shows
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Detection of Generic Human-Object Interactions in Video Streams
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Representation Learning on Visual-Symbolic Graphs for Video Understanding
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Video Activity Recognition Based on Objects Detection Using Recurrent Neural Networks
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Learning Human-Object Interactions in Videos with Optical Flow
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Mixture of Heterogeneous Attribute Analyzers for Human Action Detection
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Hand Detection and Tracking in Videos for Fine-Grained Action Recognition
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Event Analogy Based Privacy Preservation in Visual Surveillance
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Recognizing Human-Object Interactions Using Sparse Subspace Clustering
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Auxiliary criterion conversion via spatiotemporal semantic encoding and feature entropy for action recognition
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Human activity recognition in real world
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Extracting 3D Trajectories of Objects from 2D Videos using Particle Filter
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Recognizing activities with cluster-trees of tracklets
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Modélisation structurée et reconnaissance des actions humaines dans les vidéos
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Spatial techniques for video understanding
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Human Object Interaction Recognition
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