| 煤矿井下视频多目标轨迹跟踪算法研究 |
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| 引用本文： | 贾澎涛,贾 伟.煤矿井下视频多目标轨迹跟踪算法研究[J].计算机工程与应用,2018,54(2):222-227. |
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| 作者姓名： | 贾澎涛 贾 伟 |
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| 作者单位： | 西安科技大学 计算机科学与技术学院，西安 710054 |
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| 摘 要： | 为了提高煤矿井下监控视频的目标识别准确率，对运动目标进行有效跟踪，将小波变换和背景差分法相结合，对Camshift算法进行改进，提出了适用于煤矿井下视频多目标轨迹跟踪算法。首先采用小波三层变换对视频图像进行去噪处理，得到低频图像。然后再进行背景差分运算，检测出运动目标。最后采用Camshift算法对运动目标进行跟踪处理。实验结果表明，改进的Camshift算法减少了原始Camshift算法在初始候选目标时的随机性，提高了目标检测和跟踪的准确率，为煤矿的安全生产提供了保证。
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| 关 键 词： | 多目标跟踪 离散小波变换 背景差分法 Camshift算法 |
Recherche algorithm on coal mine multi-target trajectory tracking |
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| JIA Pengtao,JIA Wei.Recherche algorithm on coal mine multi-target trajectory tracking[J].Computer Engineering and Applications,2018,54(2):222-227. |
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| Authors: | JIA Pengtao JIA Wei |
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| Affiliation: | School of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China |
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| Abstract: | In order to improve object recognition accuracy of video surveillance in the coal mine and to track moving targets effectively, wavelet transform is combined with background subtraction to improve Camshift algorithm, which applies to video surveillance in the coal mine. Firstly, three decomposition principles of wavelet of wavelet transform are adopted for image de-noising to get low-frequency images. Then, background subtraction is operated to detect moving targets. Finally, the improved Camshift algorithm is applied to track moving object in the video. Simulation results show that the improved Camshift algorithm reduces the randomness when initializing the candidate targets in original Camshift and improves the accuracy of object recognition and tracking, which ensures safe production of coal mine. |
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| Keywords: | multi-target tracking discrete wavelet transform background subtraction Camshift algorithm |
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