Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Learning the Statistics of People in Images and Video
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
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FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Statistical Background Subtraction for a Mobile Observer
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Epitomic analysis of appearance and shape
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Tracking with Adaptive Feature Selection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast occluded object tracking by a robust appearance filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the international workshop on Workshop on multimedia information retrieval
Posterior probability measure for image matching
Pattern Recognition
Mathematical model of blob matching and modified Bhattacharyya coefficient
Image and Vision Computing
Graph-based transductive learning for robust visual tracking
Pattern Recognition
Multi-target tracking in time-lapse video forensics
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Closed-loop adaptation for robust tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Background subtraction for automated multisensor surveillance: a comprehensive review
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Fusing multiple video sensors for surveillance
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Backtracking: Retrospective multi-target tracking
Computer Vision and Image Understanding
Color invariant SURF in discriminative object tracking
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Co-trained generative and discriminative trackers with cascade particle filter
Computer Vision and Image Understanding
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This paper conceives of tracking as the developing distinction of a foreground against the background. In this manner, fast changes in the object or background appearance can be dealt with. When modelling the target alone (and not its distinction from the background), changes of lighting or changes of viewpoint can invalidate the internal target model. As the main contribution, we propose a new model for the detection of the target using foreground/background texture discrimination. The background is represented as a set of texture patterns. During tracking, the algorithm maintains a set of discriminant functions each distinguishing one pattern in the object region from background patterns in the neighborhood of the object. The idea is to train the foreground/background discrimination dynamically, that is while the tracking develops. In our case, the discriminant functions are efficiently trained online using a differential version of Linear Discriminant Analysis (LDA). Object detection is performed by maximizing the sum of all discriminant functions. The method employs two complementary sources of information: it searches for the image region similar to the target object, and simultaneously it seeks to avoid background patterns seen before. The detection result is therefore less sensitive to sudden changes in the appearance of the object than in methods relying solely on similarity to the target. The experiments show robust performance under severe changes of viewpoint or abrupt changes of lighting.