Object Tracking with Gradient Part-Based Models

Yang Li; Jia Bao Wang; Jian Jiang Lu; Zhuang Miao; Peng Fei Fang

doi:10.4028/www.scientific.net/AMR.490-495.905

Paper Titles

One Kind of Flight Control System Design for Separation Section in Cruise Missile
p.885

Study of Shock Control of Gear-Shifting in Automatic Transmission
p.890

The Situation and Developing Prospects of Embedded Systems
p.895

DSP-Based Intelligent Split-Phase Self-Adaption Reactive Power Compensation Controller
p.900

Object Tracking with Gradient Part-Based Models
p.905

One Electric Motor System for Steering Hydraulic Pump and Braking Air Pump in HEV BuS
p.910

New Types of Veriﬁably Encrypted Signature Schemes
p.914

Multi-Dimension and Structure Element Edge-Detection Based on Mathematical Morphology
p.919

Derivation of Magnetostrictive Losses from Admittance Spectra
p.922

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 490-495Object Tracking with Gradient Part-Based Models

Object Tracking with Gradient Part-Based Models

Abstract:

Partial occlusion and non-rigid variation are challenging problems in object tracking. To address this problem, robust gradient part-based models are proposed for object tracking in this paper. Our models constructed multiple well-chosen parts based on the gradient energy map of the object. And the local optimization algorithm, mean shift, is used to search the best locations of the multiple parts, which can be used to rectify the location of the tracked object by weighted feedback. Meanwhile, the models of the root and parts are updated online, which can improve tracking accuracy and robustness. Further, our models are easy to be embedded into different tracking algorithms and we implement the mean shift based on gradient part-based models. Experiments results show that our gradient part-based models are robust enough for object tracking, even though the objects are non-rigid or occluded.

You might also be interested in these eBooks

Mechatronics and Intelligent Materials II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

905-909

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.490-495.905

Citation:

Cite this paper

Online since:

March 2012

Authors:

Yang Li, Jia Bao Wang, Jian Jiang Lu, Zhuang Miao, Peng Fei Fang

Keywords:

Gradient Part-Based Models, Mean-Shift, Object Occlusion, Visual Object Tracking

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] B., Ben, R. Ian, Stable multi-target tracking in real-time surveillance video, In CVPR, (2011).

Google Scholar

[2] A. Yilmaz, O. Javed, and M. Shah. Object tracking: a survey, ACM Computing Surveys, 38 (2006) 1-45.

DOI: 10.1145/1177352.1177355

Google Scholar

[3] S. Agarwal, A. Awan, and D. Roth, Learning to detect objects in images via a sparse, part based representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (2004) 1475-1490.

DOI: 10.1109/tpami.2004.108

Google Scholar

[4] P. Perez, C. Hue, J. Vermaak, M. Gangnet, Color-based probabilistic tracking, In ECCV, (2002).

Google Scholar

[5] Z. Fan, Y. Wu, and M. Yang. Multiple collaborative kernel tracking. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), (2005).

DOI: 10.1109/cvpr.2005.242

Google Scholar

[6] Adam, E. Rivlin, I. Shimshoni, Robust fragments-based tracking using the integral histogram, In CVPR, 2006, pp.798-805.

DOI: 10.1109/cvpr.2006.256

Google Scholar

[7] P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan, Object detection with discriminatively trained part based models, TPAMI, 32 (2010) 1627-1645.

DOI: 10.1109/tpami.2009.167

Google Scholar