摘 要
随着计算机视觉技术的不断发展，复杂场景下的目标跟踪与识别成为智能监控、无人驾驶等领域的关键技术。本研究旨在解决复杂场景中光照变化、遮挡、姿态变化等因素对目标跟踪与识别的影响，提出了一种基于深度学习框架的多模态融合目标跟踪与识别方法。该方法通过引入时空注意力机制和自适应特征融合模块，有效提升了模型在复杂环境下的鲁棒性和准确性。实验结果表明，在多个公开数据集上，所提方法相较于传统算法及现有先进方法，在跟踪精度和识别准确率方面均有显著提升，特别是在极端条件下表现更为突出。此外，本研究还构建了一个包含多种复杂场景的大规模数据集，为后续研究提供了宝贵的资源。
关键词：目标跟踪与识别 多模态融合 时空注意力机制
Abstract
With the continuous development of computer vision technology, target tracking and recognition in complex scenes has become the key technology in the fields of intelligent monitoring and unmanned driving. This study aims to solve the influence of illumination change, occlusion, posture change and other factors on target tracking and recognition in complex scenes, and proposes a multi-modal fusion target tracking and recognition method based on deep learning fr amework. By introducing the spatiotemporal attention mechanism and the adaptive feature fusion module, this method effectively improves the robustness and accuracy of the model in complex environments. The experimental results show that compared with the traditional algorithms and the existing advanced methods, the proposed method has a significant improvement in the tracking accuracy and recognition accuracy, especially in extreme conditions. Furthermore, this study constructs a large-scale dataset with multiple complex scenarios, providing a valuable resource for subsequent studies.
Keyword:Target Tracking And Recognition Multi-Modality Fusion Spatiotemporal Attention Mechanism
目 录
1绪论 1
1.1复杂场景下视觉技术的背景与意义 1
1.2目标跟踪与识别的研究现状综述 1
1.3本文研究方法与技术路线 1
2复杂环境下的目标检测技术 2
2.1动态背景下目标的特征提取 2
2.2多尺度目标检测算法优化 3
2.3遮挡情况下的目标检测策略 3
3基于深度学习的目标跟踪方法 4
3.1深度神经网络在跟踪中的应用 4
3.2跨视角目标跟踪的技术挑战 4
3.3实时性与准确性的平衡方案 5
4复杂场景下的目标识别技术 5
4.1不同光照条件下的识别稳定性 6
4.2多类别目标的高效分类方法 6
4.3场景语义信息的融合利用 7
结论 7
参考文献 9
致谢 10
