-
摘要: 提出了一种新的基于特征不确定性度量的多特征融合跟踪算法. 首先, 针对粒子滤波跟踪算法中特征鉴别能力较弱且粒子分布相对分散时容易造成目标丢失的事实, 本文定义了一种新的特征不确定度量方法, 该度量可以在线调整不同类型特征对跟踪结果的贡献. 同时, 针对乘性和加性特征融合跟踪算法方法中存在的缺陷, 提出了一种自适应的多特征融合方法, 融合的结果既突出了状态后验分布中目标真实状态对应的峰值, 又对噪声不敏感, 从而提高了目标跟踪的鲁棒性. 各种场景下的实验结果比较表明: 新的融合跟踪算法比单特征跟踪、 乘性融合跟踪和加性融合跟踪有着更好的稳定性和鲁棒性.Abstract: This paper presents a novel tracking algorithm that fuses multiple features based on feature uncertainty measurement. It is based on the fact that tracking failure of particle filter often happens in the cases of low discriminative abilities of the observed features and disperse distributions of the sampled particles. To handle this failure, we first define a new feature uncertainty measurement method to adaptively adjust the relative contributions of different features. Then we introduce a self-adaptive feature fusion strategy to overcome the shortcomings of product and sum fusion ones. This strategy effectively sharpens the distribution of the fused posterior, and makes the tracking less sensitive to noises. Thereby, the tracking robustness is improved. An extensive number of comparative experiments show that the proposed algorithm is more stable and robust than the single feature, multiplicative fusion, and additive fusion tracking algorithms.
-
Key words:
- Object tracking /
- uncertainty measurement /
- particle filter /
- multiple features fusion
-
[1] Hou Zhi-Qiang, Han Chong-Zhao. A survey of visual tracking. Acta Automatica Sinica, 2006, 32(4): 603-617(侯志强, 韩崇昭. 视觉跟踪技术综述. 自动化学报, 2006, 32(4): 603-617)[2] Perez P, Hue C, Vermaak J, Gangnet M. Color-based probabilistic tracking. In: Proceedings of the 7th European Conference on Computer Vision. London, UK: Springer, 2002. 661-675[3] Kim B G, Park D J. Unsupervised video object segmentation and tracking based on new edge features. Pattern Recognition Letters, 2004, 25(15): 1731-1742 [4] Baker S, Matthews I. Lucas-Kanade 20 years on: a unifying framework. International Journal of Computer Vision, 2004, 56(3): 221-255 [5] Bastos R, Dias J M S. Fully automated texture tracking based on natural features extraction and template matching. In: Proceedings of the ACM SIGCHI International Conference on Advances in Computer Entertainment Technology. New York, USA: ACM, 2005. 180-183[6] Du W, Piater J. A probabilistic approach to integrating multiple cues in visual tracking. In: Proceedings of the 10th Europe on Conference on Computer Vision. Berlin, Germany: Springer, 2008. 225-238[7] Lin Hai-Feng, Ma Yu-Feng, Song Tao. Research on object tracking algorithm based on SIFT. Acta Automatica Sinica, 2010, 36(8): 1204-1208(蔺海峰, 马宇峰, 宋涛. 基于SIFT特征目标跟踪算法研究. 自动化学报, 2010, 36(8): 1204-1208) [8] Yilmaz A, Javed O, Shah M. Object tracking: a survey. ACM Computing Surveys, 2006, 38(4): 13-58 [9] Wang Yong-Zhong, Liang Yan, Zhao Chun-Hui, Pan Quan. Kernel-based tracking based on adaptive fusion of multiple cues. Acta Automatica Sinica, 2008, 34(4): 393-399(王永忠, 梁彦, 赵春晖, 潘泉. 基于多特征自适应融合的核跟踪方法. 自动化学报, 2008, 34(4): 393-399)[10] Birchfield S. Elliptical head tracking using intensity gradients and color histograms. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Santa Barbara, USA: IEEE, 1998. 232-237[11] Li P H, Chaumette F. Image cues fusion for object tracking based on particle filter. In: Proceedings of the 3rd International Workshop on Articulated Motion and Deformable Objects. Palma de Mallorca, Spain: Springer, 2004. 99-107[12] Wang X, Tang Z M. Modified particle filter-based infrared pedestrian tracking. Infrared Physics and Technology, 2010, 53(4): 280-287 [13] Zhong Xiao-Pin, Xue Jian-Ru, Zheng Nan-Ning, Ping Lin-Jiang. An adaptive fusion strategy based multiple-cue tracking. Journal of Electronics and Information Technology, 2007, 29(5): 1017-1021(钟小品, 薛建儒, 郑南宁, 平林江. 基于融合策略自适应的多线索跟踪方法. 电子与信息学报, 2007, 29(5): 1017-1021)[14] Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188 [15] Isard M, Blake A. Condensation--conditional density propagation for visual tracking. International Journal of Computer Vision, 1998, 29(1): 5-28 [16] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 886-893[17] Perez P, Vermaak J, Blake A. Data fusion for visual tracking with particles. Proceedings of the IEEE, 2004, 92(3): 495-513
点击查看大图
计量
- 文章访问数: 2782
- HTML全文浏览量: 44
- PDF下载量: 1530
- 被引次数: 0