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The video image desensitization algorithm is a technology based on the standard "T/CAAMTB 77-2022 Automotive Transmission Video and Image Desensitization Technical Requirements and Methods", which realizes the desensitization of facial and license plate data in videos and images transmitted outside the vehicle.
This algorithm typically includes the following steps:
Face detection: Firstly, the algorithm needs to perform face detection on the input video or image. This typically involves using image processing and machine learning techniques, such as Haar cascades or deep neural networks, to detect facial regions in images.
Face Mask: Once a face is detected, the algorithm generates a mask that matches the shape and size of the face. This mask can be binary, separating the facial area from the background.
Feature replacement: After the mask is generated, the algorithm will use one or more methods to replace or mask facial features. This may include using random noise, blurring, or other methods to modify facial parts to achieve desensitization.
License plate detection and desensitization: For the desensitization process of license plates, the algorithm will first detect the position of the license plate. This can be achieved through image processing techniques such as edge detection and character segmentation. Then, blur or mask the characters on the license plate to achieve desensitization effect.
Output processing: Finally, the desensitized video or image can be output for transmission outside the vehicle.
This algorithm typically requires a large amount of training data and computing resources for training and testing. At the same time, in order to ensure the effectiveness and safety of the desensitization effect, it is necessary to conduct sufficient safety and performance testing.
The video image desensitization algorithm is a technology based on the standard "T/CAAMTB 77-2022 Automotive Transmission Video and Image Desensitization Technical Requirements and Methods", which realizes the desensitization of facial and license plate data in videos and images transmitted outside the vehicle.
This algorithm typically includes the following steps:
Face detection: Firstly, the algorithm needs to perform face detection on the input video or image. This typically involves using image processing and machine learning techniques, such as Haar cascades or deep neural networks, to detect facial regions in images.
Face Mask: Once a face is detected, the algorithm generates a mask that matches the shape and size of the face. This mask can be binary, separating the facial area from the background.
Feature replacement: After the mask is generated, the algorithm will use one or more methods to replace or mask facial features. This may include using random noise, blurring, or other methods to modify facial parts to achieve desensitization.
License plate detection and desensitization: For the desensitization process of license plates, the algorithm will first detect the position of the license plate. This can be achieved through image processing techniques such as edge detection and character segmentation. Then, blur or mask the characters on the license plate to achieve desensitization effect.
Output processing: Finally, the desensitized video or image can be output for transmission outside the vehicle.
This algorithm typically requires a large amount of training data and computing resources for training and testing. At the same time, in order to ensure the effectiveness and safety of the desensitization effect, it is necessary to conduct sufficient safety and performance testing.
