Template matching tracking method based on target decomposition

Xiu, Chunbo; Li, Hongjie

doi:10.1109/ccdc.2016.7531610

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The model-based method mainly includes template matching, expert system, decision theory, Bayes network, etc. Xia analyzed the situation knowledge base and event association in intention recognition reasoning, further studied the matching inference framework of intention recognition, proposed an intention recognition template matching method based on Dempster-Shafer evidence theory, and illustrated the possibility of the method to identify and judge the target intention with an example [26]. Aiming at target tactical intention recognition in ship command decision-making and according to the characteristics of domain knowledge, Leng proposed an algorithm for the support degree of target realtime state to the intention type based on the similarity of feature components, and the evidence theory is used to integrate the support degree of each moment to form a sequential recognition approach of target tactical intention [27].…”

Section: A Model-based Methodsmentioning

confidence: 99%

Robust Air Target Intention Recognition Based on Weight Self-Learning Parallel Time-Channel Transformer Encoder

Song,

Zhou,

Cheng

et al. 2023

IEEE Access

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Most existing air target intention recognition methods use only single-moment information, risking failure when acquiring data containing noise and many outliers. The robustness of methods that utilize continuous moment information has yet to be explored. This paper designs a robust recognition method for air target intention to address the above problems. The method takes data with noise and outliers as the object, based on a parallel time-channel Transformer Encoder and a weight self-learning unit. First, a detailed introduction to air target intention recognition and robust recognition is given, and the intention space and feature space are defined. Subsequently, the data samples are reconstructed using a fixed-step sliding window to increase the information utilized with multi-moment information as input. Finally, step-wise and channelwise correlations are extracted using a time-axis Transformer Encoder and a channel-axis Transformer Encoder, respectively, and the weights of the two branches are automatically learnt using a weight selflearning unit. This enhanced self-attention network allocates attention weights between elements in the time and channel domain sequences to capture their long-range and short-range relationships and extract recognizable representations, making it robust to outliers and noise. The experimental results show that the model's recognition accuracy and composite F1 score reach 96.9% and 0.9676, and its performance remains well when the noise level and outliers proportion increase. The ablation and comparison experiments show its advantage in accuracy over other models.

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Section: A Model-based Methodsmentioning

confidence: 99%

Robust Air Target Intention Recognition Based on Weight Self-Learning Parallel Time-Channel Transformer Encoder

Song,

Zhou,

Cheng

et al. 2023

IEEE Access

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“…However, with the target jitter and attitude change, templates are at risk of degradation. In order to avoid the impact of template degradation on template matching accuracy, a reasonable template update method is required [4]. Reference [5] uses multiple templates to match the input image at the same time, and then selects the template matching result with the highest matching degree as the tracking result.…”

Section: Introductionmentioning

confidence: 99%

An edge template matching method based on FPGA for UAV extended target tracking

Sheng,

Zhang,

et al. 2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Aiming at the problem of template degradation and low real-time tracking in the scene of target jitter and target pose change in the process of UAV extended target tracking, an edge template matching method is proposed in this paper. Specifically, the edge information of each frame image is utilized to update the target edge template by integral and enhance the robustness of the template. Besides, FPGA is adopted to realize the hardware circuit design of the proposed algorithm and optimize the tracking real-time problem. Experimental results demonstrates that the proposed method have satisfactory performance, the tracking matching rate reaches 90% and the tracking speed reaches 200 FPS.

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Implement Improved Process Design Using a Lightweight Deep Learning Model to Reduce Hardware Computational Load in Instance Segmentation: Using Apple Dataset

Choi

Jang

2022

IEEE Access

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The use of deep learning using image data has already made a lot of progress. The main function is to track or classify related objects with the model of the data set in which the learning has been conducted in the image and it is used in various fields. Therefore, the accuracy has improved a lot, but the proportion of hardware use increases rapidly because the depth of the model is increased and utilized. In this paper, we propose a process to improve results by using the lightweight model and additional procedures in the program in systems using deep learning. In the proposed system, an additional image processing process is performed to increase accuracy in the results after deep learning processing, unlike other systems. It uses residual CPU usage in desktop or embedded environments with GPU, maintains processing speed, and introduces additional image processing processes to mitigate graphic memory usage using lightweight deep learning models. As a result, a system that improves the accuracy of the object area by 6% based on red apples and 16% based on green apples can be designed to reduce hardware costs and increase performance.

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Template matching tracking method based on target decomposition

Cited by 5 publications

References 16 publications

Robust Air Target Intention Recognition Based on Weight Self-Learning Parallel Time-Channel Transformer Encoder

Robust Air Target Intention Recognition Based on Weight Self-Learning Parallel Time-Channel Transformer Encoder

An edge template matching method based on FPGA for UAV extended target tracking

Implement Improved Process Design Using a Lightweight Deep Learning Model to Reduce Hardware Computational Load in Instance Segmentation: Using Apple Dataset

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