IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is a scholarly archival journal published monthly. This journal covers traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. Read the full scope of TPAMI.
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From the December 2017 issue
Online Object Tracking, Learning and Parsing with And-Or Graphs
By Tianfu Wu, Yang Lu, and Song-Chun Zhu
This paper presents a method, called AOGTracker, for simultaneously tracking, learning and parsing (TLP) of unknown objects in video sequences with a hierarchical and compositional And-Or graph (AOG) representation. The TLP method is formulated in the Bayesian framework with a spatial and a temporal dynamic programming (DP) algorithms inferring object bounding boxes on-the-fly. During online learning, the AOG is discriminatively learned using latent SVM  to account for appearance (e.g., lighting and partial occlusion) and structural (e.g., different poses and viewpoints) variations of a tracked object, as well as distractors (e.g., similar objects) in background. Three key issues in online inference and learning are addressed: (i) maintaining purity of positive and negative examples collected online, (ii) controling model complexity in latent structure learning, and (iii) identifying critical moments to re-learn the structure of AOG based on its intrackability. The intrackability measures uncertainty of an AOG based on its score maps in a frame. In experiments, our AOGTracker is tested on two popular tracking benchmarks with the same parameter setting: the TB-100/50/CVPR2013 benchmarks  ,  , and the VOT benchmarks  —VOT 2013, 2014, 2015 and TIR2015 (thermal imagery tracking). In the former, our AOGTracker outperforms state-of-the-art tracking algorithms including two trackers based on deep convolutional network  ,  . In the latter, our AOGTracker outperforms all other trackers in VOT2013 and is comparable to the state-of-the-art methods in VOT2014, 2015 and TIR2015.
Editorials and Announcements
- We are pleased to announce that Sven Dickinson, a professor in the Department of Computer Science at the University of Toronto, Canada, has been named the new Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence starting in 2017.
- According to Clarivate Analytics' 2016 Journal Citation Report, TPAMI has an impact factor of 8.329.
- Incoming EIC Editorial (Jan 2017)
- State of the Journal (Jan 2017)
- State of the Journal (Feb 2016)
- State of the Journal (Jan 2015)
- Editor's Note (June 2013)
- Farewall State of the Journal (Jan 2013)
- Editor's Note (Jan 2013)
- Editor's Note (May 2012)
- Editor's Note (February 2012)
- State of the Journal (January 2012)
- Best of CVPR 2015 (April 2017)
- Special Issue on Multimodal Human Pose Recovery and Behavior Analysis (August 2016)
- Special Section on CVPR 2014 (July 2016)
- Special Section on CVPR 2013 (April 2016)
- Special Issue on Higher Order Graphical Models in Computer Vision (July 2015)
- Special Issue on Bayesian Nonparametrics (Feb 2015)
- TPAMI CVPR Special Section (Dec 2013)
- Special Section on Learning Deep Architectures (Aug 2013)
- In Memoriam: Mark Everingham (Nov 2012)
- Introduction to the Special Section on IEEE Conference on Computer Vision and Pattern Recognition (September 2012)
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