Abstract
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.
Abstract (translated)
该项目旨在开发一个强大的视频监控系统，该系统可以根据活动检测将视频分割成较小的片段。例如，它使用CCTV录像记录重大事件，如人员或窃贼出现，从而优化存储并使数字搜索更容易。该项目采用了最新的对象检测和跟踪技术，包括卷积神经网络（CNNs）如YOLO、SSD 和 Faster R-CNN 以及递归神经网络（RNNs）和长短时记忆网络（LSTMs），以实现高精度的检测，并捕捉时间依赖关系。该方法通过高斯混合模型（GMM）和光流法如Lucas-Kanade来实现自适应背景建模，用于检测运动。多尺度和上下文分析被用来改善不同尺寸对象和环境下的检测效果。一种结合统计模型和深度学习模型的混合运动分割策略处理复杂运动，而实时处理优化确保了高效的计算性能。使用卡尔曼滤波器和Siamese网络等跟踪方法，即使在遮挡情况下也能保持平滑的追踪。多尺度和上下文分析改进了各种尺寸对象在多个场景中的检测效果。实验结果表明，在检测和跟踪物体方面实现了高精度和召回率，并且由于实时优化和光照不变特征，处理时间和准确性有了显著提升。这项研究的影响在于其潜在能力可以改变视频监控领域，减少存储需求并通过可靠、高效的对象检测与追踪来增强安全性。
URL
https://arxiv.org/abs/2412.05331
