• Corpus ID: 9030123

Enhancing Probabilistic Appearance-Based Object Tracking with Depth Information : Object Tracking under Occlusion (情報論的学習理論と機械学習)

@article{Meshgi2013EnhancingPA,
  title={Enhancing Probabilistic Appearance-Based Object Tracking with Depth Information : Object Tracking under Occlusion (情報論的学習理論と機械学習)},
  author={Kourosh Meshgi and Yu-zhe Li and Shigeyuki Oba and Shin-ichi Maeda and Shin Ishii},
  journal={IEICE technical report. Speech},
  year={2013},
  volume={113},
  pages={85-91}
}
†Graduate School of Informatics, Kyoto University, G okasho, Uji-shi, Kyoto, 611-0011 Japan E-mail: †{meshgi-k,li-yuzhe,oba,ichi,ishii}@sys.i.kyoto-u.ac. jp Abstract Object tracking has attracted recent attention bec ause of high demands for its everyday-life applicat ions. Handling occlusions especially in cluttered environments introduced new challenges to the tracking problem; identity loss, splitting/merging, shape changes, shadows and other appearance artifacts trouble appearance-based… 
ishiilab.jp

Figures and Tables from this paper

References

SHOWING 1-10 OF 24 REFERENCES
Color-Based Probabilistic Tracking
TLDR
This work introduces a new Monte Carlo tracking technique based on the same principle of color histogram distance, but within a probabilistic framework, and introduces the following ingredients: multi-part color modeling to capture a rough spatial layout ignored by global histograms, incorporation of a background color model when relevant, and extension to multiple objects.
Probabilistic people tracking with appearance models and occlusion classification: The AD-HOC system
Depth assisted Tracking Multiple Moving Objects under Occlusion
TLDR
The proposed method can track the moving objects under the most typical and challenging occlusion scenarios and can track occluded objects in similar depth layer and in different depth layers.
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
TLDR
This work introduces two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time.
Probabilistic people tracking for occlusion handling
TLDR
This work presents a novel people tracking approach, able to cope with frequent shape changes and large occlusions, and has been used in many experiments of people behavior control in indoor situations.
BraMBLe: a Bayesian multiple-blob tracker
  • M. Isard, J. MacCormick
  • Computer Science
    Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001
  • 2001
TLDR
A multi-blob likelihood function which assigns directly comparable likelihoods to hypotheses containing different numbers of objects and a Bayesian filter for tracking multiple objects when the number of objects present is unknown and varies over time are introduced.
Robust Online Appearance Models for Visual Tracking
TLDR
A framework for learning robust, adaptive, appearance models to be used for motion-based tracking of natural objects to provide robustness in the face of image outliers, while adapting to natural changes in appearance such as those due to facial expressions or variations in 3D pose.
Contour-based object tracking with occlusion handling in video acquired using mobile cameras
TLDR
A tracking method which tracks the complete object regions, adapts to changing visual features, and handles occlusions, which has two major components related to the visual features and the object shape.
Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection
  • Bo Wu, R. Nevatia
  • Computer Science
    2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
  • 2006
TLDR
This work presents an approach based on representing humans as an assembly of four body parts and detection of the body parts in single frames which makes the method insensitive to camera motions.
An adaptive color-based particle filter
...
1
2
3
...