Model-based object tracking in monocular image sequences of road traffic scenes
International Journal of Computer Vision
Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
Saliency, Scale and Image Description
International Journal of Computer Vision
A Probabilistic Framework for Spatio-Temporal Video Representation & Indexing
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Object Tracking Using Adaptive Color Mixture Models
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Region tracking through image sequences
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Region-Based Method for Model-Free Object Tracking
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Probabilistic Space-Time Video Modeling via Piecewise GMM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation Based on Region-Tracking in Image Sequences for Traffic Monitoring
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Effective Gaussian Mixture Learning for Video Background Subtraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Density-Based Appearance Modeling for Object Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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This paper proposes a scale-consistent feature density estimation method based on Gaussian Mixture Model (GMM) for robustly tracking video object under scale variation and partial occlusion. Scale consistency is achieved in both feature extraction and feature density estimation. Firstly, an image is partitioned into patches in the scale space that matches the scale of the local image pattern and the size that includes the most basic image pattern, from which scale consistent image features are extracted. Secondly, to invariantly estimate the feature density against the variation in image partition caused by the object's changing scale, an observational credible probability is defined for each patch and used to control its feature's contribution in the feature density estimation according to the size of the patch. Thirdly, the likelihood function defined by both the extracted features and their observational credible probability are maximized in the GMM parameter estimation. Moreover, partial occlusion on the patches which has repeated features does not affect the object's global appearance. Experiment results show that this method effectively tracks objects with scale variation and partial occlusion in the image sequence.