Asynchronous Multi-Sensor Fusion Multi-Target Tracking Method

2018 ◽  
Author(s):  
Song Liu ◽  
Han Shen-tu ◽  
Huajie Chen ◽  
Dongliang Peng ◽  
Yifang Shi
Keyword(s):  
Target Tracking ◽  
Sensor Fusion ◽  
Tracking Method ◽  
Multi Target Tracking

scholarly journals Multi-Target Tracking Using Windowed Fourier Single-Pixel Imaging

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7934
Author(s):  
Jinyu Zhang ◽  
Taiyang Hu ◽  
Xiaolang Shao ◽  
Mengxuan Xiao ◽  
Yingjiao Rong ◽  
...  
Keyword(s):  
Target Tracking ◽  
Joint Estimation ◽  
Frame Rate ◽  
Moving Targets ◽  
Tracking Method ◽  
Close Proximity ◽  
Multi Target Tracking ◽  
Video Experiment ◽  
First Time ◽  
Single Pixel

The single-pixel imaging (SPI) technique enables the tracking of moving targets at a high frame rate. However, when extended to the problem of multi-target tracking, there is no effective solution using SPI yet. Thus, a multi-target tracking method using windowed Fourier single-pixel imaging (WFSI) is proposed in this paper. The WFSI technique uses a series of windowed Fourier basis patterns to illuminate the target. This method can estimate the displacements of K independently moving targets by implementing 6K measurements and calculating 2K windowed Fourier coefficients, which is a measurement method with low redundancy. To enhance the capability of the proposed method, we propose a joint estimation approach for multi-target displacement, which solves the problem where different targets in close proximity cannot be distinguished. Using the independent and joint estimation approaches, multi-target tracking can be implemented with WFSI. The accuracy of the proposed multi-target tracking method is verified by numerical simulation to be less than 2 pixels. The tracking effectiveness is analyzed by a video experiment. This method provides, for the first time, an effective idea of multi-target tracking using SPI.

scholarly journals Multi‐sensor fusion for multi‐target tracking using measurement division

2020 ◽  
Vol 14 (9) ◽  
pp. 1451-1461
Author(s):  
Long Liu ◽  
Hongbing Ji ◽  
Wenbo Zhang ◽  
Guisheng Liao
Keyword(s):  
Target Tracking ◽  
Sensor Fusion ◽  
Multi Target Tracking

An Improved Method of Tracking the Motion of Sperm Target

2012 ◽  
Vol 241-244 ◽  
pp. 2940-2943
Author(s):  
Zhen Rong Deng ◽  
Wen Ming Huang ◽  
Dong Yu ◽  
Yin Wei Li
Keyword(s):  
Target Tracking ◽  
Nearest Neighbor ◽  
Data Association ◽  
Improved Method ◽  
Tracking Accuracy ◽  
Processing Methods ◽  
Tracking Method ◽  
Multi Target Tracking

This paper analyzes the multi-target tracking of sperm by using the improved nearest neighbor data association tracking method which predicts the position where the sperm may appear in the next frame by computing the interframe-distance and direction of the sperm. Also, in order to dealing with the situation of tracking lose and many-to-one tracking, we proposed two processing methods for sperm re-tracking of tracking lost compensation and tracking forecast minimum-range. The experiment shows this algorithm has better tracking accuracy and higher efficiency.

Efficient Multi-Target Tracking Method Based on Fusion Morphology Filtering in Traffic Video Detection

2011 ◽  
Vol 179-180 ◽  
pp. 1412-1416
Author(s):  
Wei Bin Chen ◽  
Zhi Yang Gu ◽  
Xin Zhang
Keyword(s):  
Target Tracking ◽  
Mean Shift ◽  
Tracking Method ◽  
Mean Shift Algorithm ◽  
Video Frames ◽  
Analysis Technique ◽  
Multi Target Tracking ◽  
Morphological Operator ◽  
Real Video ◽  
Morphology Filter

Aiming at the tracking real-time and accuracy problem of traffic videos, a efficient Multi-target tracking method based on Fusion morphology filter is proposed. The proposed Multi-target tracking method used by Morphologically Fused Tracking approach and combined with pyramid mean shift algorithm, quantizes the deterioration effects encountered on marble surfaces by providing the number, location and shape of deterioration spots. First the detection based on the application of morphological operator sequential filters by reconstruction with structuring elements of growing size. Then adopt a traditional tracking method such as adaptive pyramid mean shift, because it uses the pyramid analysis technique and can determine the pyramid level adaptively to decrease the number of iterations required to achieve region. We validate our approach on tracking examples using real video sequences, it can precisely partition and track the predetermined objects in video frames.

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