利用神经网络技术进行监控压缩的新方法

Nikita Mohod, Prateek Agrawal, Vishu Madaan
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引用次数: 0

摘要

闭路电视(CCTV)监控的整合在视频处理领域至关重要,它为全面监控提供了一种有效的方法。然而,这种做法面临的一个主要挑战是对存储空间的巨大需求。监控录像通常存储在硬盘驱动器中,由于存储空间有限,一段时间后就会被删除。为解决这一问题,我们提出了一种创新的 CCTV 视频压缩方法,即基于对象检测的监控压缩(ODSC)。我们的 ODSC 模型分为两个步骤:-i) 根据视频中的对象,使用神经网络方法 YOLOv5s & YOLOv7-tiny 和 Yolov8s 确定监控视频的重要帧和非重要帧 ii) 构建重要帧视频。对实验结果进行综合分析后发现,YOLOv8s 在 COCO 数据集上的检测准确率高达 99.7%,表现突出。我们的 ODSC 方法大大减少了存储空间,使用 YOLOv8s 实现了高达 96.31% 的平均压缩率,超过了现有的先进方法。
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A Novel Approach for Surveillance Compression using Neural Network Technique
The integration of closed-circuit television (CCTV) monitoring is crucial in the field of video processing, which provides an efficient method for comprehensive surveillance. However, a key challenge associated with this practice is its substantial demand for storage space. Typically, surveillance footage is stored in hard disk drives, and due to limited storage spaces, it is deleted after some time. To address this issue, an innovative method for compressing CCTV video, named object detection-based surveillance compression (ODSC), is introduced. Our ODSC model is divided into two steps: -i) depending upon the objects in the video, determine the significant and non-significant frames of surveillance video using the neural network approach YOLOv5s & YOLOv7-tiny and Yolov8s ii) construct the video of significant frames. Following a comprehensive analysis of the experimental outcomes, it is noted that YOLOv8s stands out with a remarkable detection accuracy of 99.7% on the COCO dataset. Our ODSC approach is reducing the storage space greatly and achieving an average compression ratio of up to 96.31% using YOLOv8s, which surpasses the existing state-of-the-art methods.
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