Towards Improved Observation Models for Visual Tracking: Selective Adaptation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive local exploration particle filter for object tracking
Image and Vision Computing
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Consumer Electronics
A robust particle tracker via markov chain monte carlo posterior sampling
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Scale modification through particle distribution in colour based tracking
Proceedings of the 10th European Conference on Visual Media Production
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Colour-based particle filters have been used exhaustively in the literature, given rise to multiple applications. However, tracking coloured objects through time has an important drawback, since the way in which the camera perceives the colour of the object can change. Simple updates are often used to address this problem, which imply a risk of distorting the model and losing the target. In this paper, a joint image characteristic-space tracking is proposed, which updates the model simultaneously to the object location. In order to avoid the curse of dimensionality, a Rao-Blackwellised particle filter has been used. Using this technique, the hypotheses are evaluated depending on the difference between the model and the current target appearance during the updating stage. Convincing results have been obtained in sequences under both sudden and gradual illumination condition changes.