运动图像跟踪过程中丢帧误差消除技术
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摘要
为解决传统方法不能自动对跟踪窗口的大小进行调整,造成跟踪定位误差高,无法有效消除丢帧误差的问题,提出一种新的运动图像跟踪过程中丢帧误差消除方法。通过对帧间差分法进行优化,利用每帧获取的背景部分完成背景模型的更新处理,通过当前帧和背景模型的差分获取运动范围。利用优化的Mean-shift法对运动图像进行跟踪处理,采用改进的帧差法对目标边缘进行提取,完成Mean-shift搜索窗口的更新处理,自适应调整跟踪窗口大小。通过适于P帧幅度能量模型对此刻帧与上一帧图像信息的改变程度进行描述,以体现运动图像序列运动分布情况,对运动图像跟踪中的丢帧误差情况进行描述。在此基础上求得运动图像跟踪状态矩阵,完成对丢帧状态参数的处理,将丢帧误差消除,以增强运动图像跟踪质量。结果表明,所提方法误差消除效果好,运动图像跟踪精度高。可见该方法能够对丢帧误差进行有效消除。
In order to solve the traditional method,the size of the tracking window cannot be automatically adjusted,resulting in high tracking error,and the problem of frame loss error cannot be effectively eliminated,a new method for eliminating frame error in moving image tracking was proposed. By optimizing the inter-frame difference method,the background model was processed by using the background portion acquired in each frame,and the motion range is obtained by the difference between the current frame and the background model. The moving image was tracked by the optimized mean-shift method,and the target edge was extracted by the improved frame difference method. The update process of the mean-shift search window was completed,and the tracking window size was adaptively adjusted. The degree of change of the image information of the frame and the previous frame was described by the P-frame amplitude energy model to reflect the motion distribution of the moving image sequence,and the frame loss error in the moving image tracking was described. On this basis,the moving image tracking state matrix was obtained,and the processing of the frame loss state parameters was completed,and the frame loss error was eliminated to enhance the moving image tracking quality. The results show that the proposed method has good error elimination effect and high accuracy of moving image tracking. It can be seen that the method can effectively eliminate the frame loss error.
In order to solve the traditional method,the size of the tracking window cannot be automatically adjusted,resulting in high tracking error,and the problem of frame loss error cannot be effectively eliminated,a new method for eliminating frame error in moving image tracking was proposed. By optimizing the inter-frame difference method,the background model was processed by using the background portion acquired in each frame,and the motion range is obtained by the difference between the current frame and the background model. The moving image was tracked by the optimized mean-shift method,and the target edge was extracted by the improved frame difference method. The update process of the mean-shift search window was completed,and the tracking window size was adaptively adjusted. The degree of change of the image information of the frame and the previous frame was described by the P-frame amplitude energy model to reflect the motion distribution of the moving image sequence,and the frame loss error in the moving image tracking was described. On this basis,the moving image tracking state matrix was obtained,and the processing of the frame loss state parameters was completed,and the frame loss error was eliminated to enhance the moving image tracking quality. The results show that the proposed method has good error elimination effect and high accuracy of moving image tracking. It can be seen that the method can effectively eliminate the frame loss error.
引文
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7 Wang Q,Yang Y,Wang Q,et al. The effect of human image in B2C website design:an eye-tracking study[J]. Enterprise Information Systems,2014,8(5):582-605
8 刘伟华,樊养余,雷涛.基于深度图像的运动人手检测与指尖点跟踪算法[J].计算机应用,2014,34(5):1442-1448Liu Weihua,Fan Yangyu,Lei Tao. Human fingertip detection and tracking algorithm based on depth image[J]. Journal of Computer Applications,2014,34(5):1442-1448
9 郑循江,叶志龙,杨勤利,等.一种多帧相关滤波的星敏感器像素非均匀性误差校正方法[J].空间控制技术与应用,2017,43(4):31-36Zheng Xunjiang,Ye Zhilong,Yang Qinli,et al. A correction method of non-uniformity pixel error applied to star sensor based on multiframe correlation filtering[J]. Aerospace Control and Application,2017,43(4):31-36
10 杨凯,魏本征,任晓强,等.基于深度图像的人体运动姿态跟踪和识别算法[J].数据采集与处理,2015,30(5):1043-1053Yang Kai,Wei Benzheng,Ren Xiaoqiang,et al. Depth image based human motion tracking and recognition algorithm[J]. Journal of Data Acquisition&Processing,2015,30(5):1043-1053
11 林雯.抖动状态下的运动图像跟踪方法研究[J].科技通报,2015,31(12):45-47Lin Wen. Research on motion image tracking method[J]. Bulletin of Science and Technology,2015,31(12):45-47
12 Smal I,Meijering E. Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy[J]. Medical Image Analysis,2015,24(1):163-189
13 李子印,朱明凌,陈柱.融合图像感知哈希技术的运动目标跟踪[J].中国图象图形学报,2015,20(6):795-804Li Ziyin,Zhu Mingling,Chen Zhu. Object tracking algorithm based on perception Hash technology[J]. Journal of Image and Graphics,2015,20(6):795-804
14 Figueira W,Ferrari R,Weatherby E,et al. Accuracy and precision of habitat structural complexity metrics derived from underwater photogrammetry[J]. Remote Sensing,2015,7(7):16883-16900
15 邵万开,石澄贤.动态背景下运动目标检测与跟踪研究[J].计算机测量与控制,2016,24(8):52-55Shao Wankai,Shi Chengxian. Moving object detection and tracking under dynamic background[J]. Computer Measurement&Control,2016,24(8):52-55
16 王芳,汪伟.缺帧环境下弱纹理图像的三维重建方法[J].西安工程大学学报,2016,30(4):477-482Wang Fang,Wang Wei. Three dimensional reconstruction method of weak texture image under the condition of lack of frame[J]. Journal of Xi'an Polytechnic University,2016,30(4):477-482
17 郭瑞芳.基于正交匹配追踪算法的急性运动超分辨率图像重构方法[J].科学技术与工程,2017,17(30):69-73Guo Ruifang. Super-resolution image reconstruction methods for acute movement based on orthogonal matching pursuit method[J].Science Technology and Engineering,2017,17(30):69-73
18 Liang Z,Wang H,Tao D,et al. Improving integrality of detected moving objects based on image matting[J]. Chinese Journal of Electronics,2014,23(4):742-746
19 康晓梅,穆柯楠,康贤.基于SIFT特征匹配的运动目标检测及跟踪方法[J].电子设计工程,2018,26(1):174-177Kang Xiaomei,Mu Kenan,Kang Xian. Moving objects detection and tracking based on SIFT feature matching[J]. Electronic Design Engineering,2018,26(1):174-177
20 Hoffmann M,Mada M,Carpenter T A,et al. Additional sampling directions improve detection range of wireless radiofrequency probes[J]. Magnetic Resonance in Medicine,2016,76(3):913-918
2 陈惠君.运动图像关键帧快速跟踪系统的改进[J].现代电子技术,2016,39(24):109-112Chen Huijun. Improvement in fast tracking system of moving image keyframe[J]. Modern Electronics Technique, 2016,39(24):109-112
3 张建丰.运动图像目标跟踪优化仿真[J].计算机仿真,2017,34(6):256-259Zhang Jianfeng. Moving image target tracking optimization simulation[J]. Computer Simulation,2017,34(6):256-259
4 季尔优,顾国华,柏连发,等.前景重配准的改进帧间误差最小化非均匀性校正算法[J].红外与激光工程,2014,43(5):1672-1678Ji Eryou,Gu Guohua,Bai Lianfa,et al. Improved interframe registration based least-mean-square-error non-uniformity correction algorithm by foreground re-registration[J]. Infrared and Laser Engineering,2014,43(5):1672-1678
5 Dhou S,Motai Y. Scale-invariant optical flow in tracking using a pan-tilt-zoom camera[J]. Robotica,2016,34(9):1923-1947
6 姬莉霞,李学相.基于相邻帧补偿的高速运动目标图像稳像算法及仿真[J].计算机科学,2014,41(7):310-312Ji Lixia,Li Xuexiang. Algorithm and simulation of image stabilization for high speed moving target images based on adjacent frames compensation[J]. Computer Science,2014,41(7):310-312
7 Wang Q,Yang Y,Wang Q,et al. The effect of human image in B2C website design:an eye-tracking study[J]. Enterprise Information Systems,2014,8(5):582-605
8 刘伟华,樊养余,雷涛.基于深度图像的运动人手检测与指尖点跟踪算法[J].计算机应用,2014,34(5):1442-1448Liu Weihua,Fan Yangyu,Lei Tao. Human fingertip detection and tracking algorithm based on depth image[J]. Journal of Computer Applications,2014,34(5):1442-1448
9 郑循江,叶志龙,杨勤利,等.一种多帧相关滤波的星敏感器像素非均匀性误差校正方法[J].空间控制技术与应用,2017,43(4):31-36Zheng Xunjiang,Ye Zhilong,Yang Qinli,et al. A correction method of non-uniformity pixel error applied to star sensor based on multiframe correlation filtering[J]. Aerospace Control and Application,2017,43(4):31-36
10 杨凯,魏本征,任晓强,等.基于深度图像的人体运动姿态跟踪和识别算法[J].数据采集与处理,2015,30(5):1043-1053Yang Kai,Wei Benzheng,Ren Xiaoqiang,et al. Depth image based human motion tracking and recognition algorithm[J]. Journal of Data Acquisition&Processing,2015,30(5):1043-1053
11 林雯.抖动状态下的运动图像跟踪方法研究[J].科技通报,2015,31(12):45-47Lin Wen. Research on motion image tracking method[J]. Bulletin of Science and Technology,2015,31(12):45-47
12 Smal I,Meijering E. Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy[J]. Medical Image Analysis,2015,24(1):163-189
13 李子印,朱明凌,陈柱.融合图像感知哈希技术的运动目标跟踪[J].中国图象图形学报,2015,20(6):795-804Li Ziyin,Zhu Mingling,Chen Zhu. Object tracking algorithm based on perception Hash technology[J]. Journal of Image and Graphics,2015,20(6):795-804
14 Figueira W,Ferrari R,Weatherby E,et al. Accuracy and precision of habitat structural complexity metrics derived from underwater photogrammetry[J]. Remote Sensing,2015,7(7):16883-16900
15 邵万开,石澄贤.动态背景下运动目标检测与跟踪研究[J].计算机测量与控制,2016,24(8):52-55Shao Wankai,Shi Chengxian. Moving object detection and tracking under dynamic background[J]. Computer Measurement&Control,2016,24(8):52-55
16 王芳,汪伟.缺帧环境下弱纹理图像的三维重建方法[J].西安工程大学学报,2016,30(4):477-482Wang Fang,Wang Wei. Three dimensional reconstruction method of weak texture image under the condition of lack of frame[J]. Journal of Xi'an Polytechnic University,2016,30(4):477-482
17 郭瑞芳.基于正交匹配追踪算法的急性运动超分辨率图像重构方法[J].科学技术与工程,2017,17(30):69-73Guo Ruifang. Super-resolution image reconstruction methods for acute movement based on orthogonal matching pursuit method[J].Science Technology and Engineering,2017,17(30):69-73
18 Liang Z,Wang H,Tao D,et al. Improving integrality of detected moving objects based on image matting[J]. Chinese Journal of Electronics,2014,23(4):742-746
19 康晓梅,穆柯楠,康贤.基于SIFT特征匹配的运动目标检测及跟踪方法[J].电子设计工程,2018,26(1):174-177Kang Xiaomei,Mu Kenan,Kang Xian. Moving objects detection and tracking based on SIFT feature matching[J]. Electronic Design Engineering,2018,26(1):174-177
20 Hoffmann M,Mada M,Carpenter T A,et al. Additional sampling directions improve detection range of wireless radiofrequency probes[J]. Magnetic Resonance in Medicine,2016,76(3):913-918
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