scholarly journals SIMULINK based Moving Object Detection and Blob Counting Algorithm for Traffic Surveillance

2013 ◽  
Vol 70 (17) ◽  
pp. 17-21
Author(s):  
Mayur Salve ◽  
Dinesh Repale ◽  
Sanket Shingate ◽  
Divya Shah
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Traffic Surveillance ◽  
Counting Algorithm

Enhanced The Moving Object Detection And Object TRACKING For Traffic Surveillance Using RBF-FDLNN AND CBF Algorithm

2021 ◽  
pp. 116306
Author(s):  
Ramakant Chandrakar ◽  
Rohit Raja ◽  
Rohit Miri ◽  
Upasana Sinha ◽  
Alok Kumar Singh Kushwaha ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Moving Object Detection ◽  
Moving Object ◽  
Traffic Surveillance

A Genetic Algorithm-Based Moving Object Detection for Real-time Traffic Surveillance

2015 ◽  
Vol 22 (10) ◽  
pp. 1619-1622 ◽  
Author(s):  
Giyoung Lee ◽  
Rammohan Mallipeddi ◽  
Gil-Jin Jang ◽  
Minho Lee
Keyword(s):  
Genetic Algorithm ◽  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object ◽  
Traffic Surveillance ◽  
Real Time Traffic

scholarly journals Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

2021 ◽  
Vol 10 (11) ◽  
pp. 742
Author(s):  
Xiaoyue Luo ◽  
Yanhui Wang ◽  
Benhe Cai ◽  
Zhanxing Li
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Moving Object Detection ◽  
Image Space ◽  
Moving Object ◽  
Surveillance Video ◽  
Traffic Surveillance

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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