Pedestrian detection method based on GMM background difference and combined features
A joint feature and pedestrian detection technology, applied in biometric feature recognition, image analysis, image data processing, etc., can solve the problems of low detection accuracy and slow video sequence detection speed, so as to improve pertinence, increase accuracy, reduce The effect of small detection times
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Embodiment 1
[0026] Embodiment 1: as Figure 1-2 As shown, a pedestrian detection method based on GMM background difference and joint features,
[0027] First use the mixed Gaussian model to model the background of the video image, and then perform a differential operation with the current frame of the video after obtaining the background image to obtain the position of the moving foreground object, determine the area to be detected in the image, and then use the classifier trained by the joint feature to be detected The area is detected, and finally the block diagram of the pedestrian is obtained.
[0028] The concrete steps of described method are as follows:
[0029] Step1, collecting video sequence images;
[0030] Step2, apply the mixed Gaussian model method to the sequence image collected in step Step 1 to carry out background modeling, obtain the background image;
[0031] Step3. Perform difference calculation between the current frame image and the background frame image to obta...
Embodiment 2
[0039] Embodiment 2: as Figure 1-2 As shown, a pedestrian detection method based on GMM background difference and joint features, such as Figure 1-2 Shown: The pedestrian detection method of GMM background modeling difference and joint features, the algorithm is mainly divided into two parts, one part is to extract the motion features of moving objects in the video as the area to be detected, and the other part is to use joint features for the area to be detected The trained classifier detects whether a moving object is a pedestrian or not.
[0040] Such as Figure 1-2 Shown: The pedestrian detection method based on the GMM background modeling difference and joint features uses the background difference model to extract pedestrian motion features: use the GMM method to model the background in real time to obtain a background image, which can be updated at intervals , to attenuate the effects of light changes and small disturbances in the image.
[0041] Such as Figure 1...
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