Saliency, Scale and Image Description
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Human action detection via boosted local motion histograms
Machine Vision and Applications
Multibody Structure-from-Motion in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mobile surveillance by 3D-outlier analysis
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Enhancing the point feature tracker by adaptive modelling of the feature support
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Towards space-time semantics in two frames
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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We introduce a novel local spatio-temporal descriptor intended to model the spatio-temporal behavior of a tracked object of interest in a general manner. The basic idea of the descriptor is the accumulation of histograms of an image function value through time. The histograms are calculated over a regular grid of patches inside the bounding box of the object and normalized to represent empirical probability distributions. The number of grid patches is fixed, so the descriptor is invariant to changes in spatial scale. Depending on the temporal complexity/details at hand, we introduce "first order STA descriptors" that describe the average distribution of a chosen image function over time, and "second order STA descriptors" that model the distribution of each histogram bin over time. We discuss entropy and χ2 as well-suited similarity and saliency measures for our descriptors. Our experimental validation ranges from the patch- to the object-level. Our results show that STA, this simple, yet powerful novel description of local space-time appearance is well-suited to machine learning and will be useful in videoanalysis, including potential applications of object detection, tracking, and background modeling.