Moving targets detection of static background subtraction

2010 ◽  
Vol 24 (5) ◽  
pp. 494-499 ◽  
Author(s):  
Yigang Zhang ◽  
Yang Cao ◽  
Xuezhi Xiang
Keyword(s):  
Background Subtraction ◽  
Moving Targets ◽  
Static Background

The Moving Target Detection under Complicated Background Base on BP Neural Networks

2013 ◽  
Vol 706-708 ◽  
pp. 1072-1076
Author(s):  
Xiang Dong Feng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Experimental Verification ◽  
Optimization Method ◽  
Moving Target ◽  
Moving Targets ◽  
Detection Ability ◽  
The Difference ◽  
Static Background

This paper aims at utilizing BP neural network and improved BP neural network in the extraction of moving targets from the static background and the motor background. Across introduces of L_M Optimization method to improve the BP neural network, combination the difference between the consecutive frames or difference from the background for the extraction of moving targets. The paper uses one types of image segmentation improved BP neural network for extraction of moving targets from the static background. We will obtain the same moved targets with the extraction of moving targets from the static background and the motor background. After experimental verification, this method on different objects in the context of the same detection ability is better.

scholarly journals Video Target Detection In Underground Mine Based On Background Difference And Edge Detection

2018 ◽  
Vol 232 ◽  
pp. 02023
Author(s):  
Shangfu Gong ◽  
Fengzhi Xu ◽  
Pengtao Jia
Keyword(s):  
Edge Detection ◽  
Background Subtraction ◽  
Detection Efficiency ◽  
Dynamic Environment ◽  
Underground Mine ◽  
Surveillance Video ◽  
Moving Targets ◽  
Canny Edge ◽  
Background Difference ◽  
Improved Algorithm

In view of the complex environment in the underground mine, the detection of moving targets in surveillance video often had the problems of low detection efficiency and the detection result was greatly affected by noise and shadows. A target extraction method based on fusion background subtraction, inter-frame difference and edge detection was proposed. Firstly, the method used the hybrid gaussian background modeling (GMM) to obtain the accurate background image of the dynamic environment, and the extracted moving targets by using background subtractiont. Then based on the three-frame differential and Canny edge detection, the foreground image and the moving object blob was obtained, which was combined with the background subtraction to eliminate noise and voids, and to avoid missed detection of the moving target. Finally, the shadows in the detection process were removed through pixel ratio and threshold screening, and morphological and connected domain processing were performed. Comparing the improved algorithm with the traditional algorithm, the test results show that the improved algorithm can effectively remove the noise and voids, suppress the shadow, avoid the missed detection target, and have a good detection effect.

scholarly journals Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

2019 ◽  
Vol 11 (10) ◽  
pp. 1190
Author(s):  
Wenjie Shen ◽  
Wen Hong ◽  
Bing Han ◽  
Yanping Wang ◽  
Yun Lin
Keyword(s):  
False Alarm ◽  
Target Detection ◽  
Background Subtraction ◽  
Image Sequence ◽  
False Alarms ◽  
Moving Target ◽  
Constant False Alarm Rate ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Original Algorithm

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.

Adaptive background model for non-static background subtraction by estimation of the color change ratio

2013 ◽  
Vol 9 (S1) ◽  
pp. 33-38 ◽  
Author(s):  
Jeisung Lee ◽  
Minkyu Cheon ◽  
Chang-Ho Hyun ◽  
Hyukmin Eum ◽  
Mignon Park
Keyword(s):  
Background Subtraction ◽  
Color Change ◽  
Background Model ◽  
Static Background

Moving Object Detection and Tracking Algorithm Based on Background Subtraction

2012 ◽  
Vol 263-266 ◽  
pp. 2211-2216
Author(s):  
Qing Ye ◽  
Yong Mei Zhang
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Moving Object Detection ◽  
Moving Object ◽  
Tracking Algorithm ◽  
Difference Image ◽  
Detection And Tracking ◽  
The Difference ◽  
Background Image ◽  
Static Background

Moving target detection and tracking algorithm as the core issue of computer vision and human-computer interaction is the first step of intelligent video surveillance system. Through comparing temporal difference method and background subtraction, a moving object detection and tracking algorithm based on background subtraction under static background is proposed, in order to quickly and accurately detect and identify the moving object in the intelligent monitoring system. In this algorithm, firstly, we use background acquisition method to receive the background image, then use the current frame image and the received background image to perform background subtraction in order to extract foreground object information and receive the difference image; secondly, we use threshold segmentation and morphology image processing to process the difference image in order to eliminate noises and receive the clear binary moving object image; finally, we use the centroid tracking method to track and mark the moving object. Experimental results show that the algorithm can effectively and quickly detect and track moving object from video sequence under static background. This algorithm is easily realized and has good real-time and robust, which is automated and self triggered for background updating. The algorithm can be used in driver assistance systems, motion capture, virtual reality and other fields.

Quantitative EPMA of nitrogen : A tricky element in the electron-probe microanalyzer

1992 ◽  
Vol 50 (2) ◽  
pp. 1622-1623
Author(s):  
G.F. Bastin ◽  
H.J.M. Heijligers
Keyword(s):  
Background Subtraction ◽  
Electron Probe ◽  
Detection System ◽  
Higher Order ◽  
Light Elements ◽  
Strong Suppression ◽  
Electron Probe Microanalyzer ◽  
Quantitative Epma ◽  
Peak Count ◽  
Lead Stearate

Among the ultra-light elements B, C, N, and O nitrogen is the most difficult element to deal with in the electron probe microanalyzer. This is mainly caused by the severe absorption that N-Kα radiation suffers in carbon which is abundantly present in the detection system (lead-stearate crystal, carbonaceous counter window). As a result the peak-to-background ratios for N-Kα measured with a conventional lead-stearate crystal can attain values well below unity in many binary nitrides . An additional complication can be caused by the presence of interfering higher-order reflections from the metal partner in the nitride specimen; notorious examples are elements such as Zr and Nb. In nitrides containing these elements is is virtually impossible to carry out an accurate background subtraction which becomes increasingly important with lower and lower peak-to-background ratios. The use of a synthetic multilayer crystal such as W/Si (2d-spacing 59.8 Å) can bring significant improvements in terms of both higher peak count rates as well as a strong suppression of higher-order reflections.

Sign in / Sign up

Export Citation Format

Share Document