The present invention provides a people counting method and device. The method is applied to a server building a people counting model comprising an image feature extraction sub model and an SSD classification regression sub model, and concretely comprises the steps of: inputting image frames into the image feature extraction sub model to generate an image feature graph; generating the default frame of each pixel point in the image feature graph based on the SSD classification regression sub model, obtaining the position coordinates and the probability score of each default frame, and taking the maximum probability score as a primary confidence coefficient; screening out front K default frames with the highest primary confidence coefficients as target candidate frames; performing boundingbox regression analysis and softxmax classification based on the position coordinates and the probability score of each default frame, and obtaining the coordinate position and the final confidence coefficient of each target candidate frame; and based on the non-maxima suppression algorithm, obtaining target frames, and performing statistics of the number of people in a monitoring area based on the quantity of the target frames. The method can effectively improve the timeliness of people counting.

People counting method and device

The invention proposes a pedestrian detection and tracking method based on a head-shoulder contour and a BP neural network. The method comprises the steps: firstly extracting a moving human body target in a video sequence through employing an adaptive mixed Gaussian background updating algorithm, and improving the background estimation precision through changing a learning factor of a mixed Gaussian model; secondly extracting an initial contour of an original target through employing a Canny operator, and carrying out contour clustering through combining a Mean shift algorithm, so as to obtain a completer body contour; thirdly building a head-shoulder contour model through combining a head-shoulder width-height ratio of a human body, extracting a head-shoulder contour characteristic vector, inputting the characteristic vector of the head-shoulder contour model into the BP neural network, clustering a plurality of human body head-shoulder models, and carrying out human body recognition; and finally tracking a detected pedestrian target through employing a particle filter. The method avoids misjudgment and wrong judgment because of the incompletion of a recognition target, improves the recognition accuracy of the pedestrian target, and reduces the calculation amount.

Pedestrian detection and tracking method based on head-shoulder contour and BP neural network

The invention provides a head-shoulder distance based human body detection method which is used for performing further human body detection on non-human-body objects on the basis of general human body detection methods. The method includes: firstly, determining an image region satisfying the condition of hair color; secondly, determining a clustering range of a human body head-shoulder model defined by a color region; finally, adopting a mean shift algorithm to acquire a convergent matrix constructed by convergent values in one-to-one correspondence to pixels in the clustering range, clustering all elements in the convergent matrix according to spatial information and color information of a target image successively, then clustering the pixels in the clustering range, judging whether each clustering category is accordant with the human body head-shoulder model or not, and extracting a category region contour of the human body head-shoulder model to acquire a head-shoulder contour. By the method, the drawback that error occurs during human body head-shoulder model extraction in case of coverage among humans is overcome, accuracy of human body detection is improved, and application range of human body detection is enlarged.

Head-shoulder distance based human body detection method

In the present invention, in interval shooting, a CPU of an imaging apparatus controls to repeatedly record images sequentially captured by an imaging section as a moving image at predetermined timing. This CPU controls to select to record an image captured by the imaging section as a still image or to select to record images sequentially captured by the imaging section as a moving image, in accordance with the movement of the imaging apparatus based on a detection result of an acceleration sensor or the movement of a photographic subject from images captured by the imaging section or a detection result of an infrared sensor. That is, when there is a possibility of motion blur or camera shake, moving image capturing is switched to still image photographing and a still image is recorded.

Imaging apparatus, imaging control method and storage medium

The invention provides a body recognizing method based on a head and shoulder model. The HOG character of the head and shoulder model is calculated and an SVM sorter is trained to take participate in sorting; the moving body target is extracted through a Gaussian mixture model, the body target outline is extracted based on the edge extracting algorithm, and the body head and shoulder model is obtained according to the body proportion relation; the HOG character of the head and shoulder model is sorted into a non-body target to be further processed. By means of the body recognizing method, the calculation amount is further reduced, and the recognizing time is shortened while the body recognizing rate is improved.

Body recognizing method based on head and shoulder model

Special human body recognition method based on head and shoulder model

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Special human body recognition method based on head and shoulder model