Share Email Print

Proceedings Paper

Compressed normalized block difference for object tracking
Author(s): Yun Gao; Dengzhuo Zhang; Donglan Cai; Hao Zhou; Ge Lan
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Feature extraction is very important for robust and real-time tracking. Compressive sensing provided a technical support for real-time feature extraction. However, all existing compressive tracking were based on compressed Haar-like feature, and how to compress many more excellent high-dimensional features is worth researching. In this paper, a novel compressed normalized block difference feature (CNBD) was proposed. For resisting noise effectively in a highdimensional normalized pixel difference feature (NPD), a normalized block difference feature extends two pixels in the original formula of NPD to two blocks. A CNBD feature can be obtained by compressing a normalized block difference feature based on compressive sensing theory, with the sparse random Gaussian matrix as the measurement matrix. The comparative experiments of 7 trackers on 20 challenging sequences showed that the tracker based on CNBD feature can perform better than other trackers, especially than FCT tracker based on compressed Haar-like feature, in terms of AUC, SR and Precision.

Paper Details

Date Published: 13 April 2018
PDF: 7 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960K (13 April 2018); doi: 10.1117/12.2310117
Show Author Affiliations
Yun Gao, Kunming Institute of Physics (China)
Yunnan Univ. (China)
Dengzhuo Zhang, Yunnan Univ. (China)
Donglan Cai, Yunnan Univ. (China)
Hao Zhou, Yunnan Univ. (China)
Ge Lan, Kunming Institute of Physics (China)

Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?