Face recognition and movement track judging methods under high noise
A face recognition and motion trajectory technology, applied in the field of face recognition, can solve problems such as misjudgment of face recognition technology, and achieve the effect of reducing the possibility of misjudgment
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Embodiment 1
[0027] A face recognition method for high noise, including,
[0028] Image acquisition step: Obtain multi-frame images decoded from the video stream. There are multiple video streams; in this step, the system processes each camera independently. The system decodes the video stream sent by the camera and converts the decoded data For pictures encoded in jpg format, the optimal way to acquire multi-frame pictures is at intervals. For example, for one of the cameras, assuming that it sends out 25 frames of images per second, we can acquire 5 of them per second. The picture of the frame, that is, n takes 5. Because in one second, the difference between the decoded pictures of each frame is not big, especially the difference between adjacent pictures is even smaller. For each video stream, the system acquires decoded pictures at intervals, which can reduce the processing from 25 frames per second to n frames per second. If n is less than 25, the system needs to process fewer pictu...
Embodiment 2
[0042] A high-noise-oriented motion trajectory discrimination method, comprising identifying a suspect using the face recognition method of Embodiment 1, after the identity of the suspect is confirmed, recording the suspect id, the camera where the suspect is, and where the suspect appears according to the suspect id. The timestamp of the camera. The tracking module tracks the suspect through the coordinates of the face frame and facial features. When the suspect leaves the camera, the timestamp of when the suspect leaves the camera is recorded. Here we can get a suspect in a certain camera. The time of appearance and departure of the suspect can be obtained, and the location information and real-time trajectory of the suspect can be accurately fed back. When the tracking module tracks the suspect of a certain camera, other cameras will judge whether there are other suspects in parallel through the method of embodiment 1. If there is another suspect, the tracking module will tr...
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