Finding Trajectories of Feature Points in a Monocular Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Establishing motion correspondence
CVGIP: Image Understanding
Active vision
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Temporal spatio-velocity transform and its application to tracking and interaction
Computer Vision and Image Understanding - Special issue on event detection in video
Unified Target Detection and Tracking Using Motion Coherence
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
ACM Computing Surveys (CSUR)
Image and Vision Computing
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Object tracking on FPGA-based smart cameras using local oriented energy and phase features
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Visual tracking using a pixelwise spatiotemporal oriented energy representation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Robust tracking based on pixel-wise spatial pyramid and biased fusion
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
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This paper presents a novel feature set for visual tracking that is derived from "oriented energies". More specifically, energy measures are used to capture a target's multiscale orientation structure across both space and time, yielding a rich description of its spatiotemporal characteristics. To illustrate utility with respect to a particular tracking mechanism, we show how to instantiate oriented energy features efficiently within the mean shift estimator. Empirical evaluations of the resulting algorithm illustrate that it excels in certain important situations, such as tracking in clutter with multiple similarly colored objects and environments with changing illumination. Many trackers fail when presented with these types of challenging video sequences.