Moving object detection under complex dynamic background

Wang Siming; Han Lele

doi:10.12086/oee.2018.180008

Article navigation > Opto-Electronic Engineering > 2018 Vol. 45 > No. 10 > 180008

Next Article Previous Article

Wang Siming, Han Lele. Moving object detection under complex dynamic background[J]. Opto-Electronic Engineering, 2018, 45(10): 180008. doi: 10.12086/oee.2018.180008

Citation:

Wang Siming, Han Lele. Moving object detection under complex dynamic background[J]. Opto-Electronic Engineering, 2018, 45(10): 180008. doi: 10.12086/oee.2018.180008

Moving object detection under complex dynamic background

Wang Siming,
Han Lele^,

School of Automation & Electrical Engineering, Lanzhou Jiao Tong University, Lanzhou, Gansu 730070, China

Fund Project: Supported by National Nature Science Foundation of China (61263004)

More Information

^*Corresponding author: Han Lele, E-mail: hlldyx12@163.com

Received Date 06 January 2018

Revised Date 11 May 2018

Published Date 01 October 2018

Abstract

Abstract

In order to realize fast and accurate detection of moving targets under complex dynamic background, a moving object detection method based on BRISK (binary robust invariant scalable keypoints) algorithm is proposed. Firstly, the image is divided into blocks, and the image blocks are filtered by using image entropy. Then, aiming at the problem of large number of mismatch in the process of feature matching, the k-nearest neighbor algorithm and Euclidean distance are used to perform feature matching. Finally, the improved sequential sampling consistency algorithm is used to refine the feature points and further completes the background motion compensation, and morphological processing is used to segment the moving target. Through the verification of multiple video images, the proposed algorithm removes 32.7% of the feature points based on the original BRISK algorithm and improves the matching efficiency by 75%. The proposed algorithm has faster processing speed than previous algorithms and strong anti-noise performance.
- moving object detection /
- BRISK algorithm /
- image entropy /
- Euclidean distance

FullText(HTML)

References

[1]	吴克伟, 段伟伟, 杨学志.雨夜条件下的红外可见光视频融合目标跟踪[J].仪器仪表学报, 2016, 17(5): 1131-1139. Google Scholar Yang K W, Duan W W, Yang X Z. Infra-visible video fusion object tracking under rainy night condition[J]. Chinese Journal of Scientific Instrument, 2016, 17(5): 1131-1139. Google Scholar
[2]	Akshay S. Single moving object detection and tracking using Horn-Schunck optical flow method[J]. International Journal of Applied Engineering Research, 2015, 10(11): 30135-30152. Google Scholar
[3]	刘洪彬, 常发亮.权重系数自适应光流法运动目标检测[J].光学精密工程, 2016, 24(2): 460-468. Google Scholar Liu H B, Chang F L. Moving object detection by optical flow method based on adaptive weight coefficient[J]. Optics and Precision Engineering, 2016, 24(2): 460-468. Google Scholar
[4]	于红绯, 刘威, 袁淮, 等.基于子块运动补偿的运动目标检测[J].电子学报, 2017, 45(1): 173-180. doi: 10.3969/j.issn.0372-2112.2017.01.024 CrossRef Google Scholar Yu H F, Liu W, Yuan H, et al. Moving object detection based on sub-block motion compensation[J]. Acta Electronica Sinica, 2017, 45(1): 173-180. doi: 10.3969/j.issn.0372-2112.2017.01.024 CrossRef Google Scholar
[5]	葛鹤银, 孙建红, 林楠, 等.融合小波变换及SIFT算法的去抖动运动目标检测[J].实验室研究与探索, 2016, 35(2): 119-123, 174. Google Scholar Ge H Y, Sun J H, Lin N, et al. A moving target detection approach to remove jitter by fusing wavelet transform and SIFT algorithm[J]. Research and Exploration in Laboratory, 2016, 35(2): 119-123, 174. Google Scholar
[6]	唐佳林, 郑杰锋, 李熙莹, 等.航拍视频中运动目标检测算法研究[J].计算机科学, 2017, 44(S2): 175-177, 183. Google Scholar Tang J L, Zheng J F, Li X Y, et al. Research on detecting algorithm of moving target in aerial video[J]. Computer Science, 2017, 44(S2): 175-177, 183. Google Scholar
[7]	刘威, 赵文杰, 李成, 等.基于改进ORB特征匹配的运动小目标检测[J].光电工程, 2015, 42(10): 13-20. doi: 10.3969/j.issn.1003-501X.2015.10.003 CrossRef Google Scholar Liu W, Zhao W J, Li C, et al. Detecting small moving target based on the improved ORB feature matching[J]. Opto-Electronic Engineering, 2015, 42(10): 13-20. doi: 10.3969/j.issn.1003-501X.2015.10.003 CrossRef Google Scholar
[8]	Leutenegger S, Chli M, Siegwart R Y. BRISK: binary robust invariant scalable keypoints[C]// Proceedings of 2011 IEEE International Conference on Computer Vision, 2011: 2548-2555. Google Scholar
[9]	何林阳, 刘晶红, 李刚, 等.改进BRISK特征的快速图像配准算法[J].红外与激光工程, 2014, 43(8): 2722-2727. Google Scholar He L Y, Liu J H, Li G, et al. Fast image registration approach based on improved BRISK[J]. Infrared and Laser Engineering, 2014, 43(8): 2722-2727. Google Scholar
[10]	Kriminger E, Cobb J T, Príncipe J C. Online active learning for automatic target recognition[J]. IEEE Journal of Oceanic Engineering, 2015, 40(3): 583-591. doi: 10.1109/JOE.2014.2340353 CrossRef Google Scholar
[11]	张闻宇, 李智, 王勇军.基于CenSurE-star特征的无人机景象匹配算法[J].仪器仪表学报, 2017, 38(2): 462-470. Google Scholar Zhang W Y, Li Z, Wang Y J. UAV scene matching algorithm based on CenSurE-star feature[J]. Chinese Journal of Scientific Instrument, 2017, 38(2): 462-470. Google Scholar
[12]	崔少辉, 谢征, 王刚, 等.二进制鲁棒不变尺度特征匹配电子稳像[J].光学精密工程, 2015, 23(9): 2715-2723. Google Scholar Cui S H, Xie Z, Wang G, et al. Feature matching electronic image stabilization based on binary robust invariant scalable keypionts[J]. Optics and Precision Engineering, 2015, 23(9): 2715-2723. Google Scholar

Overview

Overview

Overview: Moving object detection has been the focus of research in the field of machine vision and intelligent transportation. Its purpose is to segment the moving objects from the sequence of video images so as to make the next step for target recognition, tracking and navigation. However, under the complex dynamic background, many factors such as light changes, background interference, camera motion and so on, make the detection very poor. At present, the commonly used feature point detection algorithms include SIFT, SURF and ORB algorithm, but they cannot meet the requirements of moving target detection. BRISK algorithm has better rotation invariance, scale invariance and better robustness. BRISK algorithm is the best one in the image registration with larger blur, but the real-time performance of BRISK algorithm is worse than ORB algorithm. Aiming at the real-time and accuracy of moving object detection algorithm, this paper proposes a moving object detection algorithm based on improved BRISK feature matching. Firstly, the video frames are divided into blocks, the entropy of each sub-block is calculated. The sub-blocks are filtered by using the image entropy, so that sub-blocks whose local information is too concentrated can be removed so as to avoid the influence of excessive local feature points. Secondly, the AGAST algorithm is used to detect the feature points of the remaining sub-blocks and generates the corresponding feature descriptors. Then, the feature matching is performed according to the k-nearest neighbor algorithm, and the feature point pairs are further purified by the Euclidean distance. So as to achieve the purpose of further improving the accuracy of the algorithm, and provide reliable data for calculating the next motion parameters. An improved PROSAC method is used to extract the optimal feature points to estimate the background motion parameters, and the background motion compensation is completed by combining the six-parameter affine model. Finally, the frame difference method and morphological process to extract the moving target, and the Otsu method is used to obtain the optimal threshold to achieve a more complete segmentation of the moving target. In order to evaluate the detection effect of the algorithm, three groups of video images are used to verify the algorithm. The proposed algorithm removes 32.7% of the feature points and improves the running time of 1.1 s based on the original BRISK algorithm. The detection efficiency is better than the previous ORB algorithm to some extent, at the same time improves the matching efficiency to more than 75%, and enhances the anti-noise performance of the algorithm. The experimental results show that the proposed algorithm can improve the real-time performance and ensure the robustness of the proposed algorithm. Compared with the previous detection algorithms, this algorithm is more suitable for the detection of moving objects in the complex dynamic context.