SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Towards Improved Observation Models for Visual Tracking: Selective Adaptation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Fusion of Multiple Tracking Algorithms for Robust People Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking non-rigid, moving objects based on color cluster flow
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Indexing and Retrieval Based on Human Perceptual Color Clustering
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Object Level Frame Comparison for Video Shot Detection
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Switching observation models for contour tracking in clutter
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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The object tracking is one of the important tasks in computer vision. In this work, an object tracking method is proposed, which use the properties of the HSV colour space for representing the object. The pixels in the objects are transformed either as true colour pixels or grey colour pixels and clusters are formed either as a true colour cluster or as a gray colour cluster. The adaptive K-means clustering is applied for clustering the video frames and a suitable post processing is carried out for obtaining information about all clusters. A suitable similarity measure has been proposed by which the object level similarity between the current frame and next frame are calculated. The performance of the proposed approach is evaluated using bench mark video sequences such as PETS, SPEVI and also some proprietary videos. The performance of proposed approach is found to be encouraging compared to recently proposed approaches.