Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
Robust tracking with motion estimation and local Kernel-based color modeling
Image and Vision Computing
Pattern Recognition Letters
Generalizing the Lucas-Kanade algorithm for histogram-based tracking
Pattern Recognition Letters
Posterior probability measure for image matching
Pattern Recognition
Piecewise affine kernel tracking for non-planar targets
Pattern Recognition
Thermo-visual feature fusion for object tracking using multiple spatiogram trackers
Machine Vision and Applications
Vehicle and Person Tracking in Aerial Videos
Multimodal Technologies for Perception of Humans
Image registration based on kernel-predictability
Computer Vision and Image Understanding
A spatial-color mean-shift object tracking algorithm with scale and orientation estimation
Pattern Recognition Letters
Robust object tracking with background-weighted local kernels
Computer Vision and Image Understanding
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Visual Tracking in Occlusion Environments by Autonomous Switching of Targets
IEICE - Transactions on Information and Systems
Approximate Bayesian methods for kernel-based object tracking
Computer Vision and Image Understanding
Personnel tracking on construction sites using video cameras
Advanced Engineering Informatics
Combined Motion and Appearance Models for Robust Object Tracking in Real-Time
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Using physics engines to track objects in images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
An incremental Bhattacharyya dissimilarity measure for particle filtering
Pattern Recognition
Kernel covariance image region description for object tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Tracking articulated objects with physics engines
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An embedded audio-visual tracking and speech purification system on a dual-core processor platform
Microprocessors & Microsystems
Proceedings of the 9th ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications in Industry
Robust visual tracking based on occlusion detection and particle redistribution
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
An improvement in MSEPF for visual tracking
Artificial Life and Robotics
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
Multibandwidth kernel-based object tracking
Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering
Image and Vision Computing
Crossing road monitoring system based on adaptive decision for illegal situation
Applied Soft Computing
Multimedia Tools and Applications
About implicit and explicit shape representation
Reasoning, Action and Interaction in AI Theories and Systems
Effective appearance model and similarity measure for particle filtering and visual tracking
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Mean shift segmentation method based on hybridized particle swarm optimization
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
On pedestrian detection and tracking in infrared videos
Pattern Recognition Letters
A compact association of particle filtering and kernel based object tracking
Pattern Recognition
Robust Visual Tracking Using an Effective Appearance Model Based on Sparse Coding
ACM Transactions on Intelligent Systems and Technology (TIST)
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Orientation and scale invariant kernel-based object tracking with probabilistic emphasizing
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Robust segment-based object tracking using generalized hyperplane approximation
Pattern Recognition
Clustering via geometric median shift over Riemannian manifolds
Information Sciences: an International Journal
Multimodal MSEPF for visual tracking
Artificial Life and Robotics
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
Adaptive on-line similarity measure for direct visual tracking
Image and Vision Computing
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The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.