Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Color Space Switching for Face Tracking in Multi-Colored Lighting Environments
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the optimality of Naïve Bayes with dependent binary features
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object tracking using discriminative feature selection
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
IEEE Transactions on Image Processing
Fusing multiple video sensors for surveillance
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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This work presents a tracking algorithm based on a set of naive Bayesian classifiers. We consider tracking as a classification problem and train online a set of classifiers which distinguish a target object from the background around it. Classifiers' voting make a soft decision about class adherence for each pixel in video frame, forming a confidence map. We use the mean shift algorithm to find the nearest peak in the confidence map, with respect to the previous position of the target. The location of that peak represents the new position of the object. The temporal adaptivity of the tracker is achieved by gradual update of a target model. The results demonstrate ability of the proposed method to perform successful tracking in different environmental conditions.