Deep and Transfer Learning Approaches for Pedestrian Identification and Classification in Autonomous Vehicles

Mounsey, Alex; Khan, Asiya; Sharma, Sanjay

doi:10.3390/electronics10243159

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Mounsey et al [4] presented that pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way which avoids traffic accidents, which may result in individuals being harmed. In this work, a review of convolutional neural networks (CNNs) to tackle pedestrian detection is presented.…”

Section: Short Presentation Of the Papersmentioning

confidence: 99%

Autonomous Vehicles Technological Trends

Iclodean

Varga

Pfister³

2023

Electronics

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Section: Short Presentation Of the Papersmentioning

confidence: 99%

Autonomous Vehicles Technological Trends

Iclodean

Varga

Pfister³

2023

Electronics

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“…HOG-based methods effectively recognize simple and repetitive activities but may not always provide optimal performance for more complex and varied activities. Another popular method for human interaction recognition is based on LBP [12,13], which encodes the local texture of the image. LBP-based methods have also been effective for recognizing simple and repetitive activities but effective for recognizing and recognizing simple and repetitive activities.…”

Section: Image-based Hirmentioning

confidence: 99%

A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Bukht,

Mudawi,

Alotaibi

et al. 2023

CMC

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Human-human interaction recognition is crucial in computer vision fields like surveillance, human-computer interaction, and social robotics. It enhances systems' ability to interpret and respond to human behavior precisely. This research focuses on recognizing human interaction behaviors using a static image, which is challenging due to the complexity of diverse actions. The overall purpose of this study is to develop a robust and accurate system for human interaction recognition. This research presents a novel image-based human interaction recognition method using a Hidden Markov Model (HMM). The technique employs hue, saturation, and intensity (HSI) color transformation to enhance colors in video frames, making them more vibrant and visually appealing, especially in low-contrast or washed-out scenes. Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method. Feature extraction uses the features from Accelerated Segment Test (FAST), Oriented FAST, and Rotated BRIEF (ORB) techniques. The application of Quadratic Discriminant Analysis (QDA) for feature fusion and discrimination enables high-dimensional data to be effectively analyzed, thus further enhancing the classification process. It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities. The impressive accuracy rates of 93% and 94.6% achieved in the BIT-Interaction and UT-Interaction datasets respectively, highlight the success and reliability of the proposed technique. The proposed approach addresses challenges in various domains by focusing on frame improvement, silhouette and feature extraction, feature fusion, and HMM classification. This enhances data quality, accuracy, adaptability, reliability, and reduction of errors.

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“…Automatic data augmentation is first utilized in computer vision to train more efficient models [10], especially for small datasets in different domains. However, in the medical field, most medical knowledge is recorded in text data, such as electronic medical records.…”

Section: Introductionmentioning

confidence: 99%

MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

Shi

Zhang²,

Huang³

et al. 2022

Applied Sciences

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Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets to augment Chinese sentences. Nevertheless, the traditional text data augmentation ignores the semantics between words in sentences, besides, it has limitations in alleviating the problem of the diversity of augmented sentences. In this paper, a novel medical data augmentation (MDA) is proposed for NLP tasks, which combines the medical knowledge graph with text data augmentation to generate augmented data. Experiments on the named entity recognition task and relational classification task demonstrate that the MDA can significantly enhance the efficiency of the deep learning models compared to cases without augmentation.

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Deep and Transfer Learning Approaches for Pedestrian Identification and Classification in Autonomous Vehicles

Cited by 8 publications

References 38 publications

Autonomous Vehicles Technological Trends

Autonomous Vehicles Technological Trends

A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

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