KHẢO SÁT CÁC PHƯƠNG PHÁP ƯỚC TÍNH TƯ THẾ NGƯỜI TRONG CÁC HOẠT ĐỘNG HUẤN LUYỆN DỰA TRÊN HỌC SÂU, THIẾT KẾ FRAMEWORK CHO HỆ THỐNG HUẤN LUYỆN BẮN SÚNG THÔNG MINH=DEEP LEARNING - BASED HUMAN POSE ESTIMATION METHODS FOR TRAINING ACTIVITIES: A COMPREHENSIVE REVIEW AND FRAMEWORK DESIGN FOR INTELLIGENT SHOOTING TRAINING SYSTEMS

0 Người đánh giá. Xếp hạng trung bình 0

Tác giả: Thi Lan Nguyen, Thi Hai Anh Tran, Khanh Nghia Truong, Quoc Hung Truong, Minh Hoang Vu

Ngôn ngữ: vie

Ký hiệu phân loại:

Thông tin xuất bản: Tạp chí Khoa học & Công nghệ - Trường Đại học Công nghiệp Hà Nội, 2024

Mô tả vật lý: tr.57

Bộ sưu tập: Metadata

ID: 479299

Thêm vào giỏ

Human Pose Estimation (HPE) has witnessed significant advancements in recent years, largely propelled by the breakthroughs in deep learning techniques. This paper presents a comprehensive review of Deep Learning-Based HPE Methods in the context of trainning activities. This review begins by introducing the fundamental concepts of human pose estimation and its significance in sports and physical training. Subsequently, the paper delves into the landscape of deep learning methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, which have played a pivotal role in revolutionizing pose estimation. The core of the review lies in the analysis of state-of-the-art deep learning-based HPE methods tailored to training activities. Emphasis is placed on their accuracy, robustness to varying conditions, real-time processing capabilities, and integration with training environments. The challenges and limitations of deep learning-based HPE methods are addressed, along with ongoing research efforts to mitigate these issues. Finally, this paper proposes a shooting training model based on integrating of knowledge distillation into the context of HPE, highlighting its potential to enhance training assistance systems.Ước tính tư thế con người (HPE) đã có nhiều tiến bộ đáng kể trong những năm gần đây bởi những đột phá trong kỹ thuật học sâu. Bài viết này nhằm đánh giá toàn diện về phương pháp HPE dựa trên học sâu trong các hệ thống huấn luyện thông minh. Đánh giá bắt đầu bằng việc giới thiệu các khái niệm cơ bản về ước tính tư thế con người và tầm quan trọng của nó trong các hệ thống hỗ trợ huấn luyện thể chất. Sau đó, bài báo đi sâu vào các phương pháp học sâu, bao gồm mạng CNN, RNN và các biến thể của chúng. Cốt lõi của nghiên cứu nằm ở việc phân tích các phương pháp HPE dựa trên học sâu hiện đại, phù hợp với các hoạt động huấn luyện
nhấn mạnh vào tính chính xác của chúng với các điều kiện khác nhau, khả năng xử lý thời gian thực và tích hợp với môi trường đào tạo
đồng thời chỉ ra những thách thức và xu hướng nghiên cứu đối với các vấn đề còn tồn tại. Cuối cùng, bài báo đề xuất mô huấn luyện bắn súng dựa trên việc tích hợp chắt lọc kiến thức vào bối cảnh của HPE, nêu bật tiềm năng ứng dụng của mô hình này trong các hệ thống huấn luyện thông minh.

1. Đọc trực tuyến

1. Đọc trực tuyến

Tạo bộ sưu tập với mã QR