Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning

Zhang, Dawei; Yang, Tingting

doi:10.1155/2022/3422859

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Few-Shot Classification. Currently, many works have been proposed to address FSC [11][12][13][14][15][16][17][18][19], which can be mainly divided into three categories: initialization-based methods, metric-based methods, and hallucination-based methods.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Fine-Tuning Strategy for Few-Shot Classification

Zhao

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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Few-shot classification aims to enable the network to acquire the ability of feature extraction and label prediction for the target categories given a few numbers of labeled samples. Current few-shot classification methods focus on the pretraining stage while fine-tuning by experience or not at all. No fine-tuning or insufficient fine-tuning may get low accuracy for the given tasks, while excessive fine-tuning will lead to poor generalization for unseen samples. To solve the above problems, this study proposes a hybrid fine-tuning strategy (HFT), including a few-shot linear discriminant analysis module (FSLDA) and an adaptive fine-tuning module (AFT). FSLDA constructs the optimal linear classification function under the few-shot conditions to initialize the last fully connected layer parameters, which fully excavates the professional knowledge of the given tasks and guarantees the lower bound of the model accuracy. AFT adopts an adaptive fine-tuning termination rule to obtain the optimal training epochs to prevent the model from overfitting. AFT is also built on FSLDA and outputs the final optimum hybrid fine-tuning strategy for a given sample size and layer frozen policy. We conducted extensive experiments on mini-ImageNet and tiered-ImageNet to prove the effectiveness of our proposed method. It achieves consistent performance improvements compared to existing fine-tuning methods under different sample sizes, layer frozen policies, and few-shot classification frameworks.

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Section: Related Workmentioning

confidence: 99%

Hybrid Fine-Tuning Strategy for Few-Shot Classification

Zhao

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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“…Cultured meat producers face a range of technical challenges, and many of these are upstream from social challenges (Bryant, 2020). With the advancement of the robot industry, robots are becoming more and more widely used in the field of meat processing (Barbar et al , 2022; Khodabandehloo, 2022; Zhang and Yang, 2022; Zhao et al , 2022a). The segmentation process places utmost importance on the precise cutting of porcine belly.…”

Section: Introductionmentioning

confidence: 99%

Online path planning of pork cutting robot using 3D laser point cloud

Liu,

Ning,

et al. 2024

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Purpose The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork production, the development of efficient and robust meat cutting algorithms are hot issues. The uncertain and dynamic nature of the online porcine belly cutting imposes a challenge for the robot to identify and cut efficiently and accurately. Based on the above challenges, an online porcine belly cutting method using 3D laser point cloud is proposed. Design/methodology/approach The robotic cutting system is composed of an industrial robotic manipulator, customized tools, a laser sensor and a PC. Findings Analysis of experimental results shows that by comparing with machine vision, laser sensor-based robot cutting has more advantages, and it can handle different carcass sizes. Originality/value An image pyramid method is used for dimensionality reduction of the 3D laser point cloud. From a detailed analysis of the outward and inward cutting errors, the outward cutting error is the limiting condition for reducing the segments by segmentation algorithm.

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“…There are several realworld uses for object tracking, including security, surveillance, autonomous driving, automated traffic management, biological image analysis, and intelligent robot control [10], [11]. The goal of object tracking, like that of the majority of computer vision systems, is to identify and extract the target item from a stream of images that the camera continuously records [12]- [14]. Better object tracking is made possible by faster image processing computation.…”

Section: Introductionmentioning

confidence: 99%

Field programmable gate array based moving object tracking system for robot navigation

Tran

Pham

et al. 2023

Bulletin EEI

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This paper proposes a method in which an object tracking robot system is implemented on field programmable gate arrays (FPGAs). The OV7670 camera provides real-time object pictures to the system. To improve picture quality, images are put via the median filter phase. The item is distinguished from the backdrop based on color (red), after which it is subjected to a mathematical morphological approach of filtering to eliminate noise. To send the robot control signals, the object's (new) coordinates are found. In this method, the median filter, color separation, hardware IP cores, and morphological filter are all part of the embedded system on FPGA. Through the direct memory access (DMA) controller, these cores may communicate and perform high-speed pipeline computing at higher data rates. The entire system is executed in real-time on Xilinx's spartan-6 FPGA KIT. The results show practical and promise.

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Visual Object Tracking Algorithm Based on Biological Visual Information Features and Few-Shot Learning

Cited by 7 publications

References 26 publications

Hybrid Fine-Tuning Strategy for Few-Shot Classification

Hybrid Fine-Tuning Strategy for Few-Shot Classification

Online path planning of pork cutting robot using 3D laser point cloud

Field programmable gate array based moving object tracking system for robot navigation

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