• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of visual communication & image representation >A comparative study between single and multi-frame anomaly detection and localization in recorded video streams
【24h】

A comparative study between single and multi-frame anomaly detection and localization in recorded video streams

机译：录制视频流中单帧异常检测与定位的比较研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Video anomaly detection is usually studied by considering the spatial and temporal contexts. This paper focuses first on spatial context and shows that it can be a fast real-time solution. In the first part of this work there are two main contributions: employing a new deep network for reconstruction and introducing a new regularity scoring function. The new deep architecture is based on pyramid of input images and compared to UNet, the proposed architecture boosts AUC by 15% and the new regularity scoring function is based on SSIM. The second part employs a multiframe approach to distinguish temporal behavior anomalies. The second approach enhances the results by 7% compared to spatial anomaly detection. Comparing the two approaches, if computing power is limited and real time anomaly detection is looked for, single frame detection is preferred while multi frame analysis offers a much wider possibility of anomaly detection.
机译：视频异常检测通常通过考虑空间和时间上下文来研究。 本文首先关注空间背景，并表明它可以是一个快速的实时解决方案。 在这项工作的第一部分，有两个主要贡献：采用新的深度重建和引入新的规律性评分功能。 新的深度架构是基于输入图像的金字塔，与UNET相比，所提出的架构将AUC提升15％，新的规律性评分函数基于SSIM。 第二部分采用多帧方法来区分时间行为异常。 与空间异常检测相比，第二种方法将结果提高7％。 比较这两种方法，如果计算电源有限且实时异常检测，则优选单帧检测，而多帧分析提供了大幅度检测的更广泛的可能性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号