An individual's privacy is a significant concern in surveillance videos. Existing research work into the location of individuals on the basis of detecting their skin is focused either on different techniques for detecting human skin on protecting individuals from the consequences of applying such techniques. This paper considers both lines of research and proposes a hybrid scheme for human skin detection and subsequent privacy protection by utilizing color information in dynamically varying illumination and environmental conditions. For those purposes, dynamic and explicit skin-detection approaches are implemented, simultaneously considering multiple color-spaces, i.e. RGB, perceptual (HSV) and orthogonal (YCbCr) color-spaces, and then detecting the human skin by the proposed Combined Threshold Rule (CTR)-based segmentation. Comparative qualitative and quantitative detection results with an average 93.73% accuracy, imply that the proposed scheme achieves considerable accuracy without incurring a training cost. Once skin detection has been performed, the detected skin pixels (including false positives) are encrypted, when standard AES-CFB encryption of skin pixels is shown to be preferable compared to selective encryption of a whole video frame. The scheme preserves the behavior of the subjects within the video. Hence, subsequent image processing and behavior analysis, if required, can be performed by an authorized user. The experimental results are encouraging, as they show that the average encryption time is 8.268 s and the Encryption Space Ratio (ESR) is an average 7.25% for a high definition video (1280 × 720 pixels/frame). A performance comparison in terms of Correct Detection Rate (CDR) showed an average 91.5% for CTB-based segmentation compared to using only one color space for segmentation, such as using RGB with 85.86%, HSV with 80.93% and YCbCr with an average 84.8%, which implies that the proposed method of combining color-space skin identifications has a higher ability to detect skin accurately. Security analysis confirmed that the proposed scheme could be a suitable choice for real-time surveillance applications operating on resource-constrained devices.

个人的隐私是监视视频中的一个重要问题。现有的基于检测他们的皮肤的位置研究工作集中在检测人类皮肤的不同技术上，以保护人们免受使用此类技术的后果。本文考虑了这两个方面的研究，并提出了一种在动态变化的照明和环境条件下利用颜色信息来检测人的皮肤和随后保护隐私的混合方案。为此，实施了动态和显式的皮肤检测方法，同时考虑了多个颜色空间，即RGB，感知（HSV）和正交（YCbCr）颜色空间，然后通过建议的组合阈值规则检测人的皮肤（基于点击率）的细分。比较定性和定量检测结果的平均准确度为93.73％，这表明所提方案在不产生培训成本的情况下达到了相当高的准确度。一旦执行了皮肤检测，检测到的皮肤像素（包括误报）将被加密，这表明与对整个视频帧进行选择性加密相比，皮肤像素的标准AES-CFB加密更为可取。该方案保留了视频中主题的行为。因此，如果需要，可以由授权用户执行后续的图像处理和行为分析。实验结果令人鼓舞，因为它们显示高清晰度视频（1280×720像素/帧）的平均加密时间为8.268 s，加密空间比（ESR）平均为7.25％。与仅使用一种色彩空间进行分割相比，基于正确检测率（CDR）的性能比较显示平均为91.5％，例如使用RGB为85.86％，HSV为80.93％和YCbCr平均为84.8 ％，这意味着所提出的组合颜色空间皮肤识别的方法具有更高的能力，能够准确地检测皮肤。安全分析证实，该提议的方案对于在资源受限的设备上运行的实时监视应用可能是一个合适的选择。这意味着所提出的结合颜色空间皮肤识别的方法具有更高的准确检测皮肤的能力。安全分析证实，该提议的方案对于在资源受限的设备上运行的实时监视应用可能是一个合适的选择。这意味着所提出的结合颜色空间皮肤识别的方法具有更高的准确检测皮肤的能力。安全分析证实，该提议的方案对于在资源受限的设备上运行的实时监视应用可能是一个合适的选择。