Asymmetric Encryption of Surveillance Videos for Adaptive Threshold based Moving Object Detection

Author(s):  
Chandan Kumar ◽  
Shailendra Singh
2017 ◽  
Vol 38 (6) ◽  
pp. 537-541
Author(s):  
Shao Peng ◽  
Yang Chen ◽  
Zhang Jinmin

Author(s):  
D.P. Tripathy ◽  
K. Guru Raghavendra Reddy

Moving object detection is an important task in many computer vision classifications applications. The goal of this study is to identify a moving object detection method that provides a reliable and accurate identification of objects on the conveyor belt. In this paper, a study of the moving object detection methods is presented. Firstly, moving object detection pixel by pixel was performed using background subtraction, frame difference method. The threshold value in both background subtraction and frame difference is a fixed value, which determines the accuracy of object identification. The adaptive threshold values were calculated for both the methods to improve the accuracy. The performance of these methods was compared with the ground truth image.


2021 ◽  
Vol 10 (11) ◽  
pp. 742
Author(s):  
Xiaoyue Luo ◽  
Yanhui Wang ◽  
Benhe Cai ◽  
Zhanxing Li

Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.


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