PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Efficient tracking and ego-motion recovery using gait analysis
Signal Processing
Real Time Foreground-Background Segmentation Using a Modified Codebook Model
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Online kernel density estimation for interactive learning
Image and Vision Computing
Object tracking by bidirectional learning with feature selection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multi-Camera Tracking with Adaptive Resource Allocation
International Journal of Computer Vision
Multivariate online kernel density estimation with Gaussian kernels
Pattern Recognition
Visual tracking of multiple targets by multi-bernoulli filtering of background subtracted image data
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Correlation-based incremental visual tracking
Pattern Recognition
Hierarchical framework for robust and fast multiple-target tracking in surveillance scenarios
Expert Systems with Applications: An International Journal
Steering kernel-based video moving objects detection with local background texture dictionaries
Computers and Electrical Engineering
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
Journal of Systems Architecture: the EUROMICRO Journal
| Hi-index | 0.15 |
Visual features are commonly modeled with probability density functions in computer vision problems, but current methods such as a mixture of Gaussians and kernel density estimation suffer from either the lack of flexibility, by fixing or limiting the number of Gaussian components in the mixture, or large memory requirement, by maintaining a non-parametric representation of the density. These problems are aggravated in real-time computer vision applications since density functions are required to be updated as new data becomes available. We present a novel kernel density approximation technique based on the mean-shift mode finding algorithm, and describe an efficient method to sequentially propagate the density modes over time. While the proposed density representation is memory efficient, which is typical for mixture densities, it inherits the flexibility of non-parametric methods by allowing the number of components to be variable. The accuracy and compactness of the sequential kernel density approximation technique is illustrated by both simulations and experiments. Sequential kernel density approximation is applied to on-line target appearance modeling for visual tracking, and its performance is demonstrated on a variety of videos.