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基于差分法的均值漂移单目标跟踪 被引量:1

Moving target tracking based on frame difference method in the framework of Mean Shift
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摘要 研究了一种基于差分法原理的MS跟踪算法。当MS跟踪目标位置发生较大偏移时,通过使用差分法提取的目标形心位置对其进行修正。实验结果表明,该方法应用于运动目标的跟踪具有良好的跟踪效果。 This paper proposes a method of target tracking based on frame difference in the framework of Mean Shift. When the MS tracking center has big offset, it is corrected by the target center of gravity which is detected using frame difference. Experimental results show that the proposed method is applied in moving target tracking, which has good tracking performance.
作者 燕莎
出处 《微型机与应用》 2013年第21期37-40,44,共5页 Microcomputer & Its Applications
关键词 帧差法 目标提取 目标跟踪 均值漂移 frame difference method object detection target tracking Mean Shift
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参考文献9

  • 1王亮,胡卫明,谭铁牛.人运动的视觉分析综述[J].计算机学报,2002,25(3):225-237. 被引量:276
  • 2COMANIEIU D, RAMESH V. Mean Shift and optimal Prediction for efficient object tracking[C]. Proceedings of the IEEE Conference on hnage, 2000(3):70-73.
  • 3COMANICIU D, MEER P. Robust analysis of feature spaces: color image segmentation [M]. Los Alamitos, USA, 1997 : 750-755.
  • 4COLLINS, ROBERT T. Mean-shift blob tracking through scale space[D]. United States: Institute of Electrical and Electronics Engineers Computer Society, 2003.
  • 5彭宁嵩,杨杰,刘志,张风超.Mean-Shift跟踪算法中核函数窗宽的自动选取[J].软件学报,2005,16(9):1542-1550. 被引量:165
  • 6贾静平,张艳宁,柴艳妹,赵荣椿.目标多自由度Mean Shift序列图像跟踪算法[J].西北工业大学学报,2005,23(5):618-622. 被引量:8
  • 7FUKUNAGA K, HOSTETLER L D. The estimation of the gradient of a density function with applications in pattern recognition[J]. IEEE Transactions on Information Theory, 1975,21 (1) :32-40.
  • 8COMANICIU D, MEER P. Mean Shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(5):564-577.
  • 9李乡儒,吴福朝,胡占义.均值漂移算法的收敛性[J].软件学报,2005,16(3):365-374. 被引量:88

二级参考文献142

  • 1贾静平,赵荣椿.使用Mean Shift进行自适应序列图像目标跟踪[J].计算机应用研究,2005,22(2):247-249. 被引量:5
  • 2[25]Kohle M, Merkl D, Kastner J. Clinical gait analysis by neural networks: Issues and experiences. In: Proc IEEE Symposium on Computer-Based Medical Systems, Maribor, Slovenia, 1997. 138-143
  • 3[26]Meyer D, Denzler J, Niemann H. Model based extraction of articulated objects in image sequences for gait analysis. In: Proc IEEE International Conference on Image Processing, Santa Barbara, California 1997. 78-81
  • 4[27]McKenna S et al. Tracking groups of people. Computer Vision and Image Understanding, 2000, 80(1):42-56
  • 5[28]Karmann K, Brandt A. Moving object recognition using an adaptive background memory. In: Cappellini V ed. Time-varying Image Processing and Moving Object Recognition. 2. Elsevier, Amsterdam, The Netherlands, 1990
  • 6[29]Kilger M. A shadow handler in a video-based real-time traffic monitoring system. In: Proc IEEE Workshop on Applications of Computer Vision, Palm Springs, CA, 1992.1060-1066
  • 7[30]Stauffer C, Grimson W. Adaptive background mixture models for real-time tracking. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, 1999, 2:246-252
  • 8[31]Wren C, Azarbayejani A, Darrell T, Pentland A. Pfinder: Real-time tracking of the human body. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7):780-785
  • 9[32]Arseneau S, Cooperstock J. Real-time image segmentation for action recognition. In: Proc IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, Canada, 1999. 86-89
  • 10[33]Sun H, Feng T, Tan T. Robust extraction of moving objects from image sequences. In: Proc the Fourth Asian Conference on Computer Vision, Taiwan, 2000.961-964

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