Adaptive pyramid mean shift for global real-time visual tracking
Image and Vision Computing
Adaptable Neural Networks for Objects' Tracking Re-initialization
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Mean Shift tracking with multiple reference color histograms
Computer Vision and Image Understanding
Multimedia Tools and Applications
Spatial color histogram based center voting method for subsequent object tracking and segmentation
Image and Vision Computing
Shape based appearance model for kernel tracking
Image and Vision Computing
Local log-euclidean covariance matrix (L2ECM) for image representation and its applications
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Target Tracking Using Multiple Patches and Weighted Vector Median Filters
Journal of Mathematical Imaging and Vision
Robust registration-based tracking by sparse representation with model update
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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
Video event description in scene context
Neurocomputing
Event-driven video adaptation: A powerful tool for industrial video supervision
Multimedia Tools and Applications
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Kernel-based trackers aggregate image features within the support of a kernel (a mask) regardless of their spatial structure. These trackers spatially fit the kernel (usually in location and in scale) such that a function of the aggregate is optimized. We propose a kernel-based visual tracker that exploits the constancy of color and the presence of color edges along the target boundary. The tracker estimates the best affinity of a spatially aligned pair of kernels, one of which is color-related and the other of which is object boundary-related. In a sense, this work extends previous kernel-based trackers by incorporating the object boundary cue into the tracking process and by allowing the kernels to be affinely transformed instead of only translated and isotropically scaled. These two extensions make for more precise target localization. A more accurately localized target also facilitates safer updating of its reference color model, further enhancing the tracker's robustness. The improved tracking is demonstrated for several challenging image sequences.