Fundamentals of digital image processing
Fundamentals of digital image processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using Particles to Track Varying Numbers of Interacting People
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ACM Computing Surveys (CSUR)
Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
A particle filter for joint detection and tracking of color objects
Image and Vision Computing
International Journal of Computer Vision
Particle Filter with Multiple Motion Models for Object Tracking in Diving Video Sequences
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04
Tracking of Abrupt Motion Using Wang-Landau Monte Carlo Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain Data Association
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian approach to bandwidth selection for multivariate kernel density estimation
Computational Statistics & Data Analysis
Independent component analysis-based background subtraction for indoor surveillance
IEEE Transactions on Image Processing
Adaptive multiple object tracking using colour and segmentation cues
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
An object tracking method using particle filter and scale space model
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Game-theoretical occlusion handling for multi-target visual tracking
Pattern Recognition
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Object tracking is crucial to surveillance systems, which provide target information including position, size, and velocity. This paper presents a data association process combining two primary components of visual features and spatiotemporal prediction. In addition, the change perception and the visual distinguishability are utilized to adaptively combine the two primary components. The proposed spatiotemporal prediction is performed on several consecutive frames in order to cover the irregular motion of targets. The prediction is then filtered with a change perception mask to determine whether the candidate observations have motion or not. In addition, the level of contribution of a visual feature is adjusted by the proposed distinguishability to maintain a reward-penalty balance. The proposed method is applied to various video sequences having small targets and abrupt motions, and the experimental results show consistent tracking performance.