Tracklet descriptors for action modeling and video analysis

Authors:
Michalis Raptis;Stefano Soatto
Affiliations:
University of California, Los Angeles;University of California, Los Angeles
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Year:
2010

Citing 15
Cited 11

Fundamentals of speech recognition

Fundamentals of speech recognition
The Recognition of Human Movement Using Temporal Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
DEFORMOTION: Deforming Motion, Shape Average and the Joint Registration and Segmentation of Images

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Clustering seasonality patterns in the presence of errors

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recognizing Action at a Distance

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
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
On Space-Time Interest Points

International Journal of Computer Vision
The Function Space of an Activity

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Statistical Analysis of Dynamic Actions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision

IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Exploiting multi-level parallelism for low-latency activity recognition in streaming video

MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I

Trajectories based descriptor for dynamic events annotation

J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
Editors Choice Article: Structured learning of local features for human action classification and localization

Image and Vision Computing
Class consistent k-means: Application to face and action recognition

Computer Vision and Image Understanding
Articulated motion segmentation of point clouds by group-valued regularization

EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
LF-EME: Local features with elastic manifold embedding for human action recognition

Neurocomputing
Fast regularization of matrix-valued images

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Trajectory-Based modeling of human actions with motion reference points

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Exploiting sparse representations for robust analysis of noisy complex video scenes

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Statistics of pairwise co-occurring local spatio-temporal features for human action recognition

ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Human action recognition with salient trajectories

Signal Processing
Local scene flow by tracking in intensity and depth

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present spatio-temporal feature descriptors that can be inferred from video and used as building blocks in action recognition systems. They capture the evolution of "elementary action elements" under a set of assumptions on the image-formation model and are designed to be insensitive to nuisance variability (absolute position, contrast), while retaining discriminative statistics due to the fine-scale motion and the local shape in compact regions of the image. Despite their simplicity, these descriptors, used in conjunction with basic classifiers, attain state of the art performance in the recognition of actions in benchmark datasets.