Moving Target Detection and Tracking Based on Improved FCM Algorithm

2020 ◽  
Vol 14 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Wang Ke Feng ◽  
Sheng Xiao Chun
Keyword(s):  
Target Detection ◽  
Clustering Algorithm ◽  
Rapid Development ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Complex Situation ◽  
Fcm Algorithm ◽  
Detection And Tracking ◽  
Moving Vehicles

With the rapid development of computer intelligence technology, the majority of scholars have a great interest in the detection and tracking of moving targets in the field of video surveillance and have been involved in its research. Moving target detection and tracking has also been widely used in military, industrial control, and intelligent transportation. With the rapid progress of the social economy, the supervision of traffic has become more and more complicated. How to detect the vehicles on the road in real time, monitor the illegal vehicles, and control the illegal vehicles effectively has become a hot issue. In view of the complex situation of moving vehicles in various traffic videos, the authors propose an improved algorithm for effective detection and tracking of moving vehicles, namely improved FCM algorithm. It combines traditional FCM algorithm with genetic algorithm and Kalman filter algorithm to track and detect moving targets. Experiments show that this improved clustering algorithm has certain advantages over other clustering algorithms.

The Research of Moving Target Detection and Tracking System Based on DM6437

2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei
Keyword(s):  
Target Detection ◽  
Digital Signal Processor ◽  
Moving Objects ◽  
Tracking System ◽  
Digital Signal ◽  
Gaussian Mixture ◽  
Background Model ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

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.

Researching a Moving Target Detection Method Based on Magnetic Flux Induction Technology

Metals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1967
Author(s):  
Chaoqun Xu ◽  
Li Yang ◽  
Kui Huang ◽  
Yang Gao ◽  
Shaohua Zhang ◽  
...  
Keyword(s):  
Magnetic Flux ◽  
Magnetic Dipole ◽  
Target Detection ◽  
Electromotive Force ◽  
Detection Method ◽  
Field Tests ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Modern Warfare

The ocean is a very important arena in modern warfare where all marine powers deploy their military forces. Due to the complex environment of the ocean, underwater equipment has become a very threatening means of surprise attack in modern warfare. Therefore, the timely and effective detection of underwater moving targets is the key to obtaining warfare advantages and has important strategic significance for national security. In this paper, magnetic flux induction technology was studied with regard to the difficulty of detecting underwater concealed moving targets. Firstly, the characteristics of a magnetic target were analyzed and an equivalent magnetic dipole model was established. Secondly, the structure of the rectangular induction coil was designed according to the model, and the relationship between the target’s magnetism and the detection signal was deduced. The variation curves of the magnetic flux and the electromotive force induced in the coil were calculated by using the numerical simulation method, and the effects of the different motion parameters of the magnetic dipole and the size parameters of the coil on the induced electromotive force were analyzed. Finally, combined with the wavelet threshold filter, a series of field tests were carried out using ships of different materials in shallow water in order to verify the moving target detection method based on magnetic flux induction technology. The results showed that this method has an obvious response to moving targets and can effectively capture target signals, which verifies the feasibility of the magnetic flux induction detection technology.

scholarly journals Moving Target Detection and Tracking

2013 ◽  
Vol 06 (24) ◽  
pp. 4642-4645
Author(s):  
Zhan Xu ◽  
Du Lingyan ◽  
Lei Yuerong ◽  
Zeng Huiming ◽  
Chen jianling
Keyword(s):  
Target Detection ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

Multispectral correlation moving target detection and tracking

2003 ◽  
Author(s):  
Andrew A. Kostrzewski ◽  
Gajendra D. Savant ◽  
Paul I. Shnitser ◽  
Michael A. Piliavin ◽  
Sergey Sandomirsky ◽  
...  
Keyword(s):  
Target Detection ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

Deep Convolutional Neural Network driven Neuro-Fuzzy System for Moving Target Detection Using the Radar Signals

2021 ◽  
Author(s):  
M. Bharat Kumar ◽  
P. Rajesh Kumar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Fuzzy System ◽  
Deep Convolutional Neural Network ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Neuro Fuzzy ◽  
Deep Cnn

In radar signal processing, detecting the moving targets in a cluttered background remains a challenging task due to the moving out and entry of targets, which is highly unpredictable. In addition, detection of targets and estimation of the parameters have become a major constraint due to the lack of required information. However, the appropriate location of the targets cannot be detected using the existing techniques. To overcome such issues, this paper presents a developed Deep Convolutional Neural Network-enabled Neuro-Fuzzy System (Deep CNN-enabled Neuro-Fuzzy system) for detecting the moving targets using the radar signals. Initially, the received signal is presented to the Short-Time Fourier Transform (STFT), matched filter, radar signatures-enabled Deep Recurrent Neural Network (Deep RNN), and introduced deep CNN to locate the targets. The target location output results are integrated using the newly introduced neuro-fuzzy system to detect the moving targets effectively. The proposed deep CNN-based neuro-fuzzy system obtained effective moving target detection results by varying the number of targets, iterations, and the pulse repetition level for the metrics, like detection time, missed target rate, and MSE with the minimal values of 1.221s, 0.022, and 1,952.15.

scholarly journals High-speed moving target detection and tracking method based on speed estimation

2019 ◽  
Vol 2019 (20) ◽  
pp. 6637-6641
Author(s):  
Jinquan Zhang ◽  
Jingwen Li ◽  
Haizhong Ma ◽  
Ye Wang
Keyword(s):  
Target Detection ◽  
High Speed ◽  
Moving Target ◽  
Speed Estimation ◽  
Moving Target Detection ◽  
Tracking Method ◽  
Detection And Tracking

scholarly journals Moving target detection and tracking using passive acoustic radar

2016 ◽  
Author(s):  
Felix J. Yanovsky ◽  
Rustem B. Sinitsyn ◽  
Yevhen Chervoniak ◽  
Vitaliy Makarenko ◽  
Vadim Tokarev ◽  
...  
Keyword(s):  
Target Detection ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking ◽  
Passive Acoustic
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