Visual self-motion information contributes to passable width perception during a bike riding situation

Kuroda, Naoki; Ikeda, Kazuhiro; Teramoto, Wataru

doi:10.3389/fnins.2022.938446

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Due to its existence, human beings are more intelligent than animals. It is also the premise that humans have the capability to conduct scientific research (Kuroda et al, 2022 ). People rely on their brains to recognize the world, learn knowledge, and summarize rules.…”

Section: Introductionmentioning

confidence: 99%

Learning channel-selective and aberrance repressed correlation filter with memory model for unmanned aerial vehicle object tracking

Cui

Wu²,

Zhao³

2023

Front. Neurosci.

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To ensure that computers can accomplish specific tasks intelligently and autonomously, it is common to introduce more knowledge into artificial intelligence (AI) technology as prior information, by imitating the structure and mindset of the human brain. Currently, unmanned aerial vehicle (UAV) tracking plays an important role in military and civilian fields. However, robust and accurate UAV tracking remains a demanding task, due to limited computing capability, unanticipated object appearance variations, and a volatile environment. In this paper, inspired by the memory mechanism and cognitive process in the human brain, and considering the computing resources of the platform, a novel tracking method based on Discriminative Correlation Filter (DCF) based trackers and memory model is proposed, by introducing dynamic feature-channel weight and aberrance repressed regularization into the loss function, and by adding an additional historical model retrieval module. Specifically, the feature-channel weight integrated into the spatial regularization (SR) enables the filter to select features. The aberrance repressed regularization provides potential interference information to the tracker and is advantageous in suppressing the aberrances caused by both background clutter and appearance changes of the target. By optimizing the aforementioned two jointly, the proposed tracker could restrain the potential distractors, and train a robust filter simultaneously by focusing on more reliable features. Furthermore, the overall loss function could be optimized with the Alternative Direction Method of Multipliers (ADMM) method, thereby improving the calculation efficiency of the algorithm. Meanwhile, with the historical model retrieval module, the tracker is encouraged to adopt some historical models of past video frames to update the tracker, and it is also incentivized to make full use of the historical information to construct a more reliable target appearance representation. By evaluating the method on two challenging UAV benchmarks, the results prove that this tracker shows superior performance compared with most other advanced tracking algorithms.

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Section: Introductionmentioning

confidence: 99%

Learning channel-selective and aberrance repressed correlation filter with memory model for unmanned aerial vehicle object tracking

Cui

Wu²,

Zhao³

2023

Front. Neurosci.

View full text Add to dashboard Cite

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“…Thanks to the complex structure and mechanisms of the brain, humans have the capability to continuously learn new knowledge, perceive complex environments, and make precise decisions ( Cornelio et al, 2022 ; Kuroda et al, 2022 ). With the groundbreaking discovery of cells and continuous research in neuroscience, a variety of artificial neural networks have been proposed ( van Dyck et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Millimeter-wave radar object classification using knowledge-assisted neural network

et al. 2022

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To improve the cognition and understanding capabilities of artificial intelligence (AI) technology, it is a tendency to explore the human brain learning processing and integrate brain mechanisms or knowledge into neural networks for inspiration and assistance. This paper concentrates on the application of AI technology in advanced driving assistance system. In this field, millimeter-wave radar is essential for elaborate environment perception due to its robustness to adverse conditions. However, it is still challenging for radar object classification in the complex traffic environment. In this paper, a knowledge-assisted neural network (KANN) is proposed for radar object classification. Inspired by the human brain cognition mechanism and algorithms based on human expertise, two kinds of prior knowledge are injected into the neural network to guide its training and improve its classification accuracy. Specifically, image knowledge provides spatial information about samples. It is integrated into an attention mechanism in the early stage of the network to help reassign attention precisely. In the late stage, object knowledge is combined with the deep features extracted from the network. It contains discriminant semantic information about samples. An attention-based injection method is proposed to adaptively allocate weights to the knowledge and deep features, generating more comprehensive and discriminative features. Experimental results on measured data demonstrate that KANN is superior to current methods and the performance is improved with knowledge assistance.

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Visual self-motion information contributes to passable width perception during a bike riding situation

Cited by 2 publications

References 26 publications

Learning channel-selective and aberrance repressed correlation filter with memory model for unmanned aerial vehicle object tracking

Learning channel-selective and aberrance repressed correlation filter with memory model for unmanned aerial vehicle object tracking

Millimeter-wave radar object classification using knowledge-assisted neural network

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