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

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

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    沈阳化工大学材料科学与工程学院 沈阳 110142

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