Earticle

초록

영어

To solve the problem of tracking the trajectory of a moving object and learning a deep compact image representation in the complex environment, a novel robust incremental deep learning tracker is presented under the particle filter framework. The incremental deep classification neural network was composed of stacked denoising autoencoder, incremental feature learning and support vector machine to achieve the feature-extracting and classification of particle set. Deep learning is successfully taken to express the image representations obtained effectively. Unsupervised feature learning is used to learn generic image features and transfer learning transforms knowledge from offline training to the online tracking process. The incremental feature learning was consisted of adding features and merging features to online learn compact feature set. Linear support vector machine increases the discretion for target with similar appearance and is further tuned to adapt to appearance changes of the moving object. Compared with the state-of-the-art trackers in the complex environment, the results of experiments on variant challenging image sequences show that incremental deep learning tracker solves the problem of existent trackers more efficiently, it has better robust and more accurate, especially for occlusions, background clutter, illumination changes and appearance changes.

목차

Abstract
 1. Introduction
 2. Particle Filter
 3. Incremental Deep Classification Neural Network
  3.1. SDAE
  3.2. Linear SVM Classifier
  3.3. Incremental Feature Learning
 4. Implementation Details
 5. Experiments
  5.1. Quantitative Comparison
  5.2. Qualitative Comparison
 6. Conclusions and Future Work
 References

키워드

저자

• Shuai Cheng [ School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China ]
• Yonggang Cao [ School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China ]
• Junxi Sun [ School of Computer Science and information Technology, Northeast Normal University, Changchun, China ]
• Guangwen Liu [ School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605