基于压缩特征的鱼眼视频目标跟踪算法研究

李雅倩; 贾璐; 李海滨; 张文明; 张岩松

doi:10.11999/JEIT170745

基于压缩特征的鱼眼视频目标跟踪算法研究

doi: 10.11999/JEIT170745

基金项目:

河北省自然科学基金(F2015203212)

计量
- 文章访问数: 1353
- HTML全文浏览量: 149
- PDF下载量: 175
- 被引次数: 0
出版历程
- 收稿日期: 2017-07-21
- 修回日期: 2018-01-24
- 刊出日期: 2018-05-19

Research on Target Tracking Algorithm from Fisheye Camera Based on Compressive Sensing

Funds:

The Natural Science Foundation of Hebei Province (F2015203212)

摘要

摘要: 该文针对畸变严重的鱼眼图像中的目标跟踪，提出一种能适应尺度变化、姿态变化以及形状畸变的鱼眼视频目标跟踪的方法。该方法首先将灰度特征和相对梯度特征相结合得到目标的高维特征，然后对其平均降维得到目标的压缩特征。并根据鱼眼成像模型得到投影点的运动特性，确定目标的运动范围。为了适应尺度变化，在块匹配运动估计思想的基础上，对目标跟踪框的顶点分别进行由粗到精的定位，并在此过程中根据跟踪框的尺度相应改变压缩特征的尺度。实验结果表明：该算法在目标畸变、尺度变化、姿态变化以及局部遮挡等情况下，判断指标均优于其他对比算法。
- 目标跟踪 /
- 鱼眼成像模型 /
- 相对梯度特征 /
- 压缩特征 /
- 块匹配运动估计
Abstract: For object detection in fisheye images which present serious distortion, an object tracking method is proposed to deal with scale variance, pose change and distortion. Firstly, gray feature and gradient feature are combined to obtain a high dimensional feature of the target, then reduce its dimensionality by averaging to obtain targets compressive feature. According to fisheye imaging model, motion of object point is modeled, and range of motion of target is predicted. In order to adjust to scale variance, corner points are positioned respectively in a coarse to fine manner based on the block matching motion estimation, and the scale of compressed feature is changed along with scale change of object box. Experimental results show that the proposed algorithm is superior to other algorithms in the case of distortion, scale change, pose change and part occlusion.
- Target tracking /
- Fisheye imaging model /
- Gradient feature /
- Compression feature /
- Block matching motion estimation

HTML全文

参考文献(22)

L Lijun and WU Xuewei. Design of initial structure of fisheye lens[J]. Acta Optica Sinica, 2017, 37(2): 105-114.

吕丽军, 吴学伟. 鱼眼镜头初始结构的设计[J]. 光学学报, 2017, 37(2): 105-114.

URBAN S, LEITLOFF J, and HINZ S. Improved wide-angle, fisheye and omnidirectional camera calibration[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108(8): 72-79. doi: 10.1016/j.isprsjprs.2015.06.005.

PEREZ Y A, LOPEZ N G, and GUERRERO J. A novel hybrid camera system with depth and fisheye cameras[C]. International Conference on Pattern Recognition. Cancun, Mexico, 2017: 2789-2794. doi: 10.1109/ICPR.2016.7900058.

WALLHOFF F, ZOBL M, and RIGOLL G. Face tracking in meeting room scenarios using omnidirectional views[C]. International Conference on Pattern Recognition, Washington, USA, 2004: 933-936. doi: 10.1109/ICPR.2004. 368.

BAKSTEIN H and LEONARDIS A. Catadioptric image- based rendering for mobile robot localization[C]. International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007: 1-6. doi: 10.1109/ICCV.2007.4409199.

CAO Z. Dynamic omni-directional vision localization using a beacon tracker based on particle filter[J]. The International Society for Optical Engineering, 2007, 6764(9): 13-28. doi: 10.1117/12.733862.

BRITO J H, ANGST R, KOSER K, et al. Radial distortion self-calibration[C]. Computer Vision and Pattern Recognition, Washington, USA, 2013: 1368-1375. doi: 10.1109/CVPR. 2013.180.

CHEN X, HUANG K, and TAN T. Object tracking across non-overlapping cameras using adaptive models[C]. International Conference on Computer Vision, Berlin, Germany, 2013: 464-477. doi: 10.1007/978-3-642-37484- 5_38.

DEMONCEAUX C and VASSEUR P. Omnidirectional image processing using geodesic metric[C]. IEEE International Conference on Image Processing, Piscataway, USA, 2009: 221-224. doi: 10.1109/ICIP.2009.5414485.

BAZIN J C, YOON K J, KWEON I, et al. Particle filter approach adapted to catadioptric images for target tracking application[C]. British Machine Vision Conference, London, UK, 2009: 1-11. doi: 10.5244/C.23.37.

TAIANA M, GASPAR J, NASCIMENTO J, et al. 3D tracking by catadioptric vision based on particle filters[C]. RoboCup Robot Soccer World Cup XI, Atlanta, USA, 2007: 77-88. doi: 10.1007/978-3-540-68847-1_7.

WANG X, LI W, WANG C, et al. An improved particle filter tracking algorithm for fisheye camera[C]. Chinese Control and Decision Conference, Yinchuan, China, 2010: 329-332. doi: 10.1109/CCDC.2016.7531004.

WANG W, XU Y, WANG Y, et al. Effective weighted compressive tracking[C]. International Conference on Image and Graphics. Qingdao, China, 2013: 353-357. doi: 10.1109/ ICIG.2013.77.

汪龙. 一种新的基于自适应窗口的压缩跟踪算法[J]. 计算机与数字工程, 2016, 44(9): 1700-1704. doi: 10.3969/j.issn. 1672-9722.2016.09.016.

WANG Long. A new compressive tracking algorithm based on adaptive window[J]. Computer and Digital Engineering, 2016, 44(9): 1700-1704. doi: 10.3969/j.issn.1672-9722.2016. 09.016.

ZHANG K, ZHANG L, and YANG M H. Real-time compressive tracking[C]. European Conference on Computer Vision, Berlin, Germany, 2012: 864-877. doi: 10.1007/978-3- 642-33712-3_62.

EICHENSEER A and KAUP A. A data set providing synthetic and real-world fisheye video sequences[C]. International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, 2016: 1541-1545. doi: 10.1109/ ICASSP.2016.7471935.

ZHANG K, ZHANG L, and YANG M H. Fast compressive tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(10): 2002-2015. doi: 10.1109/ TPAMI.2014.2315808.

ZHANG K, ZHANG L, YANG M H, et al. Fast tracking via spatio-temporal context learning[C]. Computer Vision and Pattern Recognition, Oregon, USA, 2013: 1-15.

POSSEGGER H, MAUTHNER T, and BISCHOF H. In defense of color-based model-free tracking[C]. Computer Vision and Pattern Recognition, Boston, USA, 2015: 2113-2120. doi: 10.1109/CVPR.2015.7298823.

EVERINGHAM M, GOOL L, WILLIAMS C K, et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303-338. doi: 10.1007/s11263-009-0275-4.

施引文献

资源附件(0)

访问统计