Spatial-temporal difference method for detecting small moving targets in visible image background clutter

2006 ◽  
Author(s):  
Moufa Hu ◽  
Zengping Chen
Keyword(s):  
Difference Method ◽  
Temporal Difference ◽  
Moving Targets ◽  
Visible Image ◽  
Background Clutter

scholarly journals A temporal difference method for multi-objective reinforcement learning

2017 ◽  
Vol 263 ◽  
pp. 15-25 ◽  
Author(s):  
Manuela Ruiz-Montiel ◽  
Lawrence Mandow ◽  
José-Luis Pérez-de-la-Cruz
Keyword(s):  
Reinforcement Learning ◽  
Difference Method ◽  
Temporal Difference ◽  
Multi Objective

scholarly journals Research on Design of Intelligent Background Differential Model for Training Target Monitoring

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ya Liu ◽  
Fusheng Jiang ◽  
Yuhui Wang ◽  
Lu OuYang ◽  
Bo Gao ◽  
...  
Keyword(s):  
Difference Method ◽  
Detection Efficiency ◽  
Moving Targets ◽  
Subsequent Work ◽  
Differential Model ◽  
Colour Image ◽  
Huge Impact ◽  
The Difference ◽  
Target Monitoring ◽  
Background Difference

The detection of moving targets is to detect the change area in a sequence of images and extract the moving targets from the background image. It is the basis. Whether the moving targets can be correctly detected and segmented has a huge impact on the subsequent work. Aiming at the problem of high failure rate in the detection of sports targets under complex backgrounds, this paper proposes a research on the design of an intelligent background differential model for training target monitoring. This paper proposes a background difference method based on RGB colour separation. The colour image is separated into independent RGB three-channel images, and the corresponding channels are subjected to the background difference operation to obtain the foreground image of each channel. In order to retain the difference of each channel, the information of the foreground images of the three channels is fused to obtain a complete foreground image. The feature of the edge detection is not affected by light; the foreground image is corrected. From the experimental results, the ordinary background difference method uses grey value processing, and some parts of the target with different colours but similar grey levels to the background cannot be extracted. However, the method in this paper can better solve the defect of misdetection. At the same time, compared with traditional methods, it also has a higher detection efficiency.

scholarly journals Moving Target Detection in Multichannel SAR Framework using Adaptive Neuro Fuzzy Decisive Technique

2019 ◽  
Vol 8 (2) ◽  
pp. 4517-4523 ◽  
Keyword(s):  
Target Detection ◽  
Target Location ◽  
Fractional Fourier Transform ◽  
Research Area ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Neuro Fuzzy ◽  
Linguistic Rules ◽  
Background Clutter

Precise and efficacious detection of moving targets is a prominent task in on-going synthetic aperture radar (SAR) technique. The perception of moving object allows quite significant data about the situation under observation for both surveillance and intelligence activities. The task of accurately locating moving targets against strong background clutter in minimum of time is of utmost interest in the current research area. Fractional Fourier Transform (FrFT) concentrates the energy of the required chirp signal so that it can be well separated from the chirp like noise. The proposed SAR Moving Target Detection (MTD) process is based on the combination of FrFT with the adaptive-neuro fuzzy decisive technique. The correlation among the received signal and the FrFT of the received signal are computed which maximizes the required signal energy and applied to the adaptive-neuro fuzzy decisive module that detects the target location adaptively using the fuzzy linguistic rules. The simulation is performed by changing the number of targets, different Pulse repetition intervals, antenna turn velocity, iterations and the analysis is carried out based on the metrics, like detection time, missed target rate, and Mean Square Error (MSE), proving that the proposed Adaptive-Neuro Fuzzy-based MTD process detected the object in 5.0237 secs with a minimum missed target rate of 0.1210 and MSE of 23377.48.

Temporal difference method for processing dynamic speckle patterns

2010 ◽  
Vol 283 (24) ◽  
pp. 4972-4977 ◽  
Author(s):  
L. Martí-López ◽  
H. Cabrera ◽  
R.A. Martínez-Celorio ◽  
R. González-Peña
Keyword(s):  
Difference Method ◽  
Temporal Difference ◽  
Dynamic Speckle ◽  
Speckle Patterns

A ROBUST MOVING BODY RECOGNITION METHOD

2012 ◽  
Vol 11 (02) ◽  
pp. 107-114
Author(s):  
SHOUJIA WANG ◽  
WENHUI LI ◽  
BO FU ◽  
HONGYIN NI ◽  
CONG WANG
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Connected Domain ◽  
Difference Method ◽  
Recognition Accuracy ◽  
Temporal Difference ◽  
Recognition Method ◽  
Moving Body ◽  
Robust Recognition ◽  
Body Recognition

At present, moving body recognition is one of the most active areas of research in the field of computer vision and is used widely in all kinds of videos. But the recognition accuracy of these methods has changed negatively because of the complexity of the background. In this paper, we put forward a robust recognition method. First, we obtain the moving body by tripling the temporal difference method. And then we eliminate noise from these images by mathematical morphology. Finally, we use three-scanning notation method to mark and connect the connected domain. This new method is more accurate and requires less computation in real-time experiments. The experiment result also proves its robustness.

scholarly journals Intelligent Monitoring Method of Short-Distance Swimming Physical Function Fatigue Limit Mobile Calculation

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Jianxia Yin ◽  
Shimeng Huang ◽  
Lei Lei ◽  
Jing Yao
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Detection Method ◽  
Classification Problem ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Monitoring Method ◽  
Intelligent Monitoring ◽  
Frame Difference

The detection and classification of moving targets have always been a key technology in intelligent video surveillance. Current detection and classification algorithms for moving targets still face many difficulties, mainly because of the complexity of the monitoring environment and the limitations of target characteristics. Therefore, this article conducts corresponding research on moving target detection and classification in intelligent video surveillance. According to the Gaussian Mixture Background Model and Frame Difference Method, this paper proposes a moving target detection method based on GMM (Gaussians Mixture Model) and Frame Difference Method. This method first proposes a new image combination algorithm that combines GMM and frame difference method, which solves the problems of noise and voids inside the target caused by the fusion of traditional GMM and frame difference method. The moving target detection method can effectively solve the problems of incomplete moving target detection, target internal gap, and noise, and it plays a vital role in the subsequent moving target classification process. Then, the method adds image inpainting technology to compensate the moving target in space and obtain a better target shape. The innovation of this paper is that in order to solve the multiobject classification problem, a binary tree decision support vector machine based on statistical learning is constructed as a classifier for moving object classification. Improve the learning efficiency of the classifier, solve the competitive classification problem of the traditional SVM, and increase the efficiency of the mobile computing intelligent monitoring method by more than 70%.

The Extracting System Design of Small Dim Targets in Complex Background

2011 ◽  
Vol 328-330 ◽  
pp. 2324-2327
Author(s):  
Dong Sheng Liang ◽  
Zhao Hui Liu ◽  
Wen Liu
Keyword(s):  
Time Interval ◽  
Short Time Interval ◽  
Moving Targets ◽  
Visible Image ◽  
Low Snr ◽  
Hardware System ◽  
Complex Background ◽  
Straight Line ◽  
Segmentation Methods ◽  
Short Time

For the moving small dim targets in visible image sequences with low SNR and complex background, whose contained characters are simple and poor, they are extracted difficultly. This paper proposes a new method to detect and extract positions of small dim moving targets. According to the features of moving targets, in a very short time interval, the target trajectory is considered as a straight line approximately. Firstly, it makes use of threshold segmentation methods to extract the positions of targets in each frame, then building the motion line equations after the joint of multi-frame processing results. Finally, the position of small dim targets are detected out and extracted, and false targets are eliminated accurately. Hardware system was designed and the algorithm is implemented on hardware systems successfully. The experiment results show related functions of the system and extracting algorithm is feasible, the system is stable and has a strong process ability, which can effectively detect and extract small and dim target in complex background correctly.

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