Sparse Modeling of Human Actions from Motion Imagery
International Journal of Computer Vision
Mid-level features and spatio-temporal context for activity recognition
Pattern Recognition
Combining skeletal pose with local motion for human activity recognition
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
The acousticvisual emotion guassians model for automatic generation of music video
Proceedings of the 20th ACM international conference on Multimedia
Trajectory signature for action recognition in video
Proceedings of the 20th ACM international conference on Multimedia
Complex events detection using data-driven concepts
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Space-variant descriptor sampling for action recognition based on saliency and eye movements
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Action recognition robust to background clutter by using stereo vision
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Evaluating the impact of frame rate on video based human action recognition
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Multimedia event detection using segment-based approach for motion feature
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Auto learning temporal atomic actions for activity classification
Pattern Recognition
An information retrieval approach to identifying infrequent events in surveillance video
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Action recognition using canonical correlation kernels
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Exploring the similarities of neighboring spatiotemporal points for action pair matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Combining embedded accelerometers with computer vision for recognizing food preparation activities
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Exploring dense trajectory feature and encoding methods for human interaction recognition
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Learning latent spatio-temporal compositional model for human action recognition
Proceedings of the 21st ACM international conference on Multimedia
Human action recognition with salient trajectories
Signal Processing
Activity detection and recognition of daily living events
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Combining multiple sensors for event recognition of older people
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Knives are picked before slices are cut: recognition through activity sequence analysis
Proceedings of the 5th international workshop on Multimedia for cooking & eating activities
User-adaptive models for recognizing food preparation activities
Proceedings of the 5th international workshop on Multimedia for cooking & eating activities
Towards a comprehensive computational model foraesthetic assessment of videos
Proceedings of the 21st ACM international conference on Multimedia
Beauty is here: evaluating aesthetics in videos using multimodal features and free training data
Proceedings of the 21st ACM international conference on Multimedia
Human action recognition by fast dense trajectories
Proceedings of the 21st ACM international conference on Multimedia
Smart multi-modal marine monitoring via visual analysis and data fusion
Proceedings of the 2nd ACM international workshop on Multimedia analysis for ecological data
Exploring STIP-based models for recognizing human interactions in TV videos
Pattern Recognition Letters
Multiple scale-specific representations for improved human action recognition
Pattern Recognition Letters
Detecting bipedal motion from correlated probabilistic trajectories
Pattern Recognition Letters
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Editor's Choice Article: Human activity recognition in videos using a single example
Image and Vision Computing
Classifying web videos using a global video descriptor
Machine Vision and Applications
Local scene flow by tracking in intensity and depth
Journal of Visual Communication and Image Representation
Language-motivated approaches to action recognition
The Journal of Machine Learning Research
Robust action recognition using local motion and group sparsity
Pattern Recognition
Matching mixtures of curves for human action recognition
Computer Vision and Image Understanding
A tensor motion descriptor based on histograms of gradients and optical flow
Pattern Recognition Letters
Graph-based approach for human action recognition using spatio-temporal features
Journal of Visual Communication and Image Representation
Evaluating multimedia features and fusion for example-based event detection
Machine Vision and Applications
Key observation selection-based effective video synopsis for camera network
Machine Vision and Applications
Multimedia Event Detection Using Segment-Based Approach for Motion Feature
Journal of Signal Processing Systems
Silhouette-based human action recognition using SAX-Shapes
The Visual Computer: International Journal of Computer Graphics
Retina enhanced SURF descriptors for spatio-temporal concept detection
Multimedia Tools and Applications
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Feature trajectories have shown to be efficient for representing videos. Typically, they are extracted using the KLT tracker or matching SIFT descriptors between frames. However, the quality as well as quantity of these trajectories is often not sufficient. Inspired by the recent success of dense sampling in image classification, we propose an approach to describe videos by dense trajectories. We sample dense points from each frame and track them based on displacement information from a dense optical flow field. Given a state-of-the-art optical flow algorithm, our trajectories are robust to fast irregular motions as well as shot boundaries. Additionally, dense trajectories cover the motion information in videos well. We, also, investigate how to design descriptors to encode the trajectory information. We introduce a novel descriptor based on motion boundary histograms, which is robust to camera motion. This descriptor consistently outperforms other state-of-the-art descriptors, in particular in uncontrolled realistic videos. We evaluate our video description in the context of action classification with a bag-of-features approach. Experimental results show a significant improvement over the state of the art on four datasets of varying difficulty, i.e. KTH, YouTube, Hollywood2 and UCF sports.