用深度学习与混合模型实现高效动态场景追踪，减少存储压力。
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking
- 融合CNN、RNN与统计模型，结合多尺度分析提升检测精度。
- 实测在复杂场景下检测准确率显著提升，处理速度优化明显。
- 适合安防系统开发，尤其关注实时性与低光照环境的应用。
本项目旨在构建一个鲁棒的视频监控系统，通过活动检测将视频分割为小片段。利用如闭路电视（CCTV）等来源的视频，仅记录关键事件（如人员出现或盗窃行为），从而优化存储并简化数字搜索。系统采用最新的目标检测与跟踪技术，包括卷积神经网络（CNN）如YOLO、SSD和Faster R-CNN，以及循环神经网络（RNN）和长短期记忆网络（LSTM），以实现高精度检测并捕捉时序依赖关系。通过高斯混合模型（GMM）和Lucas-Kanade光流法进行自适应背景建模与运动检测。采用多尺度与上下文分析，提升不同尺寸物体及多样环境下的检测性能。提出一种混合运动分割策略，结合统计与深度学习模型，有效应对复杂运动。通过实时处理优化确保高效计算。使用卡尔曼滤波器与孪生网络等方法，在遮挡情况下仍保持稳定跟踪。多尺度与上下文分析提升了对多种场景下不同大小目标的检测能力。实验结果表明，系统在检测与跟踪中具有高精度与高召回率，得益于实时优化与光照不变特征，处理时间大幅缩短，准确性显著提高。该研究有望推动视频监控变革，降低存储需求，增强安全防护能力。
原文摘要 · Abstract (English)
This project aims to develop a robust video surveillance system, which can segment videos into smaller clips based on the detection of activities. It uses CCTV footage, for example, to record only major events-like the appearance of a person or a thief-so that storage is optimized and digital searches are easier. It utilizes the latest techniques in object detection and tracking, including Convolutional Neural Networks (CNNs) like YOLO, SSD, and Faster R-CNN, as well as Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs), to achieve high accuracy in detection and capture temporal dependencies. The approach incorporates adaptive background modeling through Gaussian Mixture Models (GMM) and optical flow methods like Lucas-Kanade to detect motions. Multi-scale and contextual analysis are used to improve detection across different object sizes and environments. A hybrid motion segmentation strategy combines statistical and deep learning models to manage complex movements, while optimizations for real-time processing ensure efficient computation. Tracking methods, such as Kalman Filters and Siamese networks, are employed to maintain smooth tracking even in cases of occlusion. Detection is improved on various-sized objects for multiple scenarios by multi-scale and contextual analysis. Results demonstrate high precision and recall in detecting and tracking objects, with significant improvements in processing times and accuracy due to real-time optimizations and illumination-invariant features. The impact of this research lies in its potential to transform video surveillance, reducing storage requirements and enhancing security through reliable and efficient object detection and tracking.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。