基于颜色属性直方图的尺度目标跟踪算法研究

毕笃彦; 库涛; 查宇飞; 张立朝; 杨源

doi:10.11999/JEIT150921

基于颜色属性直方图的尺度目标跟踪算法研究

doi: 10.11999/JEIT150921

1.
(空军工程大学航空航天工程学院西安 710038) ②(空军工程大学空管领航学院西安 710051)

基金项目:

国家自然科学基金(61472442, 61372167), 陕西省青年科技新星项目(2015KJXX-46)

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- 文章访问数: 1527
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- 被引次数: 0
出版历程
- 收稿日期: 2015-08-07
- 修回日期: 2016-01-22
- 刊出日期: 2016-05-19

Scale-adaptive Object Tracking Based on Color Names Histogram

1.
(Institute of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi&rsquo
2.
(Institute of ATC Navigation, Air Force Engineering University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61472442, 61372167), The Young Star Science and Technology Program of Shaanxi (2015KJXX-46)

摘要

摘要: 利用目标颜色信息的跟踪算法，容易受到环境光照、尺度变化、相似背景等因素的干扰，导致跟踪任务失败。为了克服以上问题，该文提出一种基于颜色属性空间的鲁棒尺度目标跟踪算法。该算法首先将原始的RGB颜色空间映射到颜色属性(Color Names, CN)空间，减少目标颜色在跟踪过程中受环境变化影响。然后采用一种背景加权约束的颜色属性直方图，来抑制相似背景的干扰。最后，为了解决目标尺度变化带来的影响，先用梯度上升法粗略估计尺度，再用约束项精确求解尺度，并利用反向一致性检验，进一步提高尺度估计的准确性。该文选取了5段典型视频进行实验，并与相关算法进行比较。结果表明所提算法能够消除环境光照、阴影、相似背景和尺度变化等因素所带来的影响，在中心位置误差和跟踪成功率性能指标上，优于其它算法。
- 目标跟踪 /
- 颜色属性 /
- 背景加权抑制 /
- 尺度自适应
Abstract: Tracking effects of algorithms using color information are easily interfered by background clustering, illumination and scale changes, which can result in tracking failure. To solve these problems, an efficient model is proposed to project original RGB color space to a more robust color spaceColor Names (CN) feature space. Furthermore, objects are represented by background weighted color names histogram, and thus the similar background patches around the target are suppressed. Moreover, a two-step tuning way is adapted to estimate the scale by coarse tuning with gradient ascent and fine tuning with constrained items. Back-forward scale check is also used to ensure the precision of scale estimation. 5 representative videos are chosen to examine the proposed algorithms with four others. The results show that the proposed approach is robust to illumination variation, shadows, background clustering, and scale changes. The central distance error and tracking accuracy of the proposed approach also outperform the contrast algorithms.
- Object tracking /
- Color Names (CN) /
- Background weighted suppression /
- Scale-adaptive

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参考文献(17)

POSSEGGER H, MAUTHNER T, and BISCHOF H. In defense of color-based model-free tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 2113-2120.

ORON S, BAR-HILLEL A, LEVI D, et al. Locally orderless tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, USA, 2012: 1940-1947.

胡良梅, 段琳琳, 张旭东, 等. 融合颜色信息与深度信息的运动目标检测方法[J]. 电子与信息学报, 2014, 36(9): 2047-2052. doi: 10.3724/SP.J.1146.2013.01763.

HU Liangmei, DUAN Linlin, ZHANG Xudong, et al. Moving object detection based on the fusion of color and depth information[J]. Journal of Electronics Information Technology, 2014, 36(9): 2047-2052. doi: 10.3724/SP.J.1146. 2013.01763.

MEER P, RAMESH V, and COMANICIU D. Kernel-based object tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-575.

张红颖, 胡正. 融合局部三值数量和色度信息的均值漂移跟踪[J]. 电子与信息学报, 2014, 36(3): 624-630. doi: 10.3724/ SP.J.1146.2013.01155.

ZHANG Hongying and HU Zheng. Mean shift tracking method combing local ternary number with hue information[J]. Journal of Electronics Information Technology, 2014, 36(3): 624-630. doi: 10.3724/SP.J.1146. 2013.01155.

Van de WEIJER J, SCHMID C, and VERBEEK J. Learning color names from real-world Images[C]. IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, 2007: 1-8.

Van de WEIJER J, SCHMID C, VERBEEK J, et al. Learning color names for real-world applications[J]. IEEE Transactions on Image Processing, 2009, 18(7): 1512-1523.

KHAN F S, Van de WEIJER J, and VANRELL M. Modulating shape features by color attention for object recognition[J]. International Journal of Computer Vision, 2012, 98(1): 49-64.

KHAN F S, ANWER R M, Van de WEIJER J, et al. Color attributes for object detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, USA, 2012: 3306-3313.

DANELLJAN M, KHAN F S, FELSBERG M, et al. Adaptive color attributes for real-time visual tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 1090-1097.

COMANICIU D, RAMESH V, and MEER P. Real-time tracking of non-rigid objects using mean shift[C]. IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, USA, 2000: 142-149.

NING J, ZHANG L, ZHANG D, et al. Scale and orientation adaptive mean shift tracking[J]. IET Computer Vision, 2012, 6(1): 52-61.

VOJIR T, NOSKOVA J, and MATAS J. Robust scale- adaptive mean-shift for tracking[J]. Pattern Recognition Letters, 2014, 49(1): 250-258.

WU Y, LIM J, and YANG M H. Online object tracking: A benchmark[C]. IEEE Conference on Computer Vision and Pattern Recognition, Oregon, USA, 2013: 2411-2418.

BOUGUET J Y. Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm[J]. Intel Corporation, 2001, 5(4): 1-10.

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