摘 要
本文探讨了基于深度学习的行为识别与动作分析系统设计与优化。行为识别与动作分析在智能监控、智能驾驶等领域具有重要应用价值，本文通过概述相关理论，分析存在的问题与挑战，并提出相应的优化对策。研究指出，行为识别与动作分析面临的主要挑战包括数据集标注问题、行为和动作的多样性与复杂性、实时性和效率问题以及个体差异问题。为应对这些挑战，本文提出了数据增强技术、模型结构优化、联合学习策略和硬件加速技术等优化对策。最后，本文对研究进行总结，并展望了未来的发展方向，包括深度学习模型优化、上下文信息建模、多模态信息融合、行为动机与意图理解以及实际应用场景的深度学习技术应用。随着技术的不断进步，行为识别与动作分析领域有望实现更准确、更实时的分析能力。
关键词：动作分析；数据集；捕捉；神经网络
DESIGN AND OPTIMIZATION OF BEHAVIOR RECOGNITION AND ACTION ANALYSIS SYSTEM BASED ON DEEP LEARNING
ABSTRACT
This paper discusses the design and optimization of behavior recognition and action analysis system based on deep learning. Behavior recognition and action analysis have important application value in intelligent monitoring, intelligent driving and other fields. This paper summarizes the relevant theories, analyzes the existing problems and challenges, and puts forward the corresponding optimization countermeasures. The research points out that the main challenges of behavior recognition and action analysis include data set annotation, the diversity and complexity of behaviors and actions, real-time and efficiency, and individual differences. To deal with these challenges, this paper puts forward some optimization strategies, such as data enhancement, model structure optimization, joint learning strategy and hardware acceleration technology. Finally, this paper summarizes the research and looks forward to the future development direction, including deep learning model optimization, context information modeling, multi-modal information fusion, behavioral motivation and intention understanding, and the application of deep learning technology in practical application scenarios. As technology continues to advance, the field of behavior recognition and action analysis is expected to achieve more accurate and real-time analysis capabilities.
KEY WORDS：Action Analysis; Data Set; Catch; Neural Network
目 录
摘 要 I
ABSTRACT II
第1章 绪论 2
1.1 研究背景及意义 2
1.2 国内外发展现状 2
第2章 相关理论概述 4
2.1 行为识别理论概述 4
2.2 动作分析理论概述 4
2.3 深度学习在行为识别与动作分析中的应用 5
第3章 存在的问题与挑战 7
3.1 数据集标注问题 7
3.2 多样性和复杂性问题 7
3.3 实时性和效率问题 8
3.4 个体差异问题 8
第4章 相应的优化对策 10
4.1 数据增强技术 10
4.2 模型结构优化 10
4.3 联合学习策略 11
4.4 硬件加速技术 11
第5章 总结与未来展望 13
参考文献 14
致 谢 15
