scholarly journals Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 152612-152623 ◽  
Author(s):  
Junhui Zuo ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.


2018 ◽  
Vol 10 (5) ◽  
pp. 742 ◽  
Author(s):  
Wenjie Shen ◽  
Yun Lin ◽  
Lingjuan Yu ◽  
Feiteng Xue ◽  
Wen Hong

2019 ◽  
Vol 11 (10) ◽  
pp. 1190
Author(s):  
Wenjie Shen ◽  
Wen Hong ◽  
Bing Han ◽  
Yanping Wang ◽  
Yun Lin

Spaceborne spotlight SAR mode has drawn attention due to its high-resolution capability, however, the studies about moving target detection with this mode are less. The paper proposes an image sequence-based method entitled modified logarithm background subtraction to detect ground moving targets with Gaofen-3 Single Look Complex (SLC) spotlight SAR images. The original logarithm background subtraction method is designed by our team for airborne SAR. It uses the subaperture image sequence to generate a background image, then detects moving targets by using image sequence to subtract background. When we apply the original algorithm to the spaceborne spotlight SAR data, a high false alarm problem occurs. To tackle the high false alarm problem due to the target’s low signal-to-noise-ratio (SNR) in spaceborne cases, several improvements are made. First, to preserve most of the moving target signatures, a low threshold CFAR (constant false alarm rate) detector is used to get the coarse detection. Second, because the moving target signatures have higher density than false detections in the coarse detection, a modified DBSCAN (density-based spatial-clustering-of-applications-with-noise) clustering method is then adopted to reduce false alarms. Third, the Kalman tracker is used to exclude the residual false detections, due to the real moving target signature having dynamic behavior. The proposed method is validated by real data, the shown results also prove the feasibility of the proposed method for both Gaofen-3 and other spaceborne systems.


2014 ◽  
Vol 998-999 ◽  
pp. 759-762 ◽  
Author(s):  
Xin Ping Wu ◽  
Min Cang Fu

In order to overcome the problem that the single-Gauss model is poor of anti-interference and Gaussian Mixture model is poor of real-time, we present the double modeling algorithm of moving target detection. We use three frame difference method to distinguish the invariant region and complex region in background. And then we use single-Gauss modeling to model the invariant background while the complex region of background would be modeled with Gaussian Mixture modeling. It is more effective than the single-Gauss model and more efficient than the Gaussian Mixture model .The experimental results show that the improved algorithm is superior to the traditional single Gauss model or Gaussian Mixture model. It can detect moving target more quickly and accurately, with good robustness and real-time.


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