分析动态目标追踪与轨迹预测技术现状及应用挑战
Trajectory Prediction in Dynamic Object Tracking: A Critical Study
- 综合评估特征、分割、估计和学习等多类追踪方法
- 指出当前技术在真实场景中仍面临泛化能力弱、依赖数据等问题
- 适合自动驾驶、安防等领域的研究人员参考
本研究深入分析了动态对象追踪（DOT）与轨迹预测（TP）的最新进展，涵盖特征驱动、分割驱动、估计驱动和学习驱动等多种方法，评估其在实际场景中的有效性、部署难度与局限性。这些技术在自动驾驶、安防监控、医疗健康和工业自动化等领域显著提升了安全性与效率。尽管取得进展，仍存在泛化能力不足、计算效率低、数据依赖性强及伦理问题等挑战。研究提出未来应聚焦多模态数据融合、语义信息整合与上下文感知系统构建，并发展兼顾隐私保护的伦理框架。
原文摘要 · Abstract (English)
This study provides a detailed analysis of current advancements in dynamic object tracking (DOT) and trajectory prediction (TP) methodologies, including their applications and challenges. It covers various approaches, such as feature-based, segmentation-based, estimation-based, and learning-based methods, evaluating their effectiveness, deployment, and limitations in real-world scenarios. The study highlights the significant impact of these technologies in automotive and autonomous vehicles, surveillance and security, healthcare, and industrial automation, contributing to safety and efficiency. Despite the progress, challenges such as improved generalization, computational efficiency, reduced data dependency, and ethical considerations still exist. The study suggests future research directions to address these challenges, emphasizing the importance of multimodal data integration, semantic information fusion, and developing context-aware systems, along with ethical and privacy-preserving frameworks.
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