The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Texture Classification Using Three Circular Filters
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
International Journal of Computer Vision
Comparing evaluation protocols on the KTH dataset
HBU'10 Proceedings of the First international conference on Human behavior understanding
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-similarity and points of interest in textured images
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Evaluation of background subtraction techniques for video surveillance
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Fast Action Detection via Discriminative Random Forest Voting and Top-K Subvolume Search
IEEE Transactions on Multimedia
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We propose to model the human activity in space-time as a Bag of Words model, characterized by a new spatio-time interest points descriptor based on a combination of 3D gradient and a method based on textural appearance. The texture capturing approach we propose is based on the assumption that what is meaningful in textured man-made structures is the property of being symmetrical, like correspondence in size, shape, and relative position of parts on opposite sides of a dividing line or median plane or about a center or axis. Differently from recent approaches, we show how the combination of the 3D gradient and textural appearance improves the recognition accuracy, compared to methods based on silhouette or not textural feature extractors.