Pedestrian detection method and device, electronic equipment and storage medium
A technology for pedestrian detection and images to be detected is applied in the fields of pedestrian detection methods, electronic equipment and storage media, and devices, and can solve the problems of lack of analysis and reduced accuracy of pedestrian detection by algorithms, so as to improve the accuracy, reduce the possibility, Enhance the effect of feature angles
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Embodiment 1
[0025] figure 1 A schematic flowchart of the pedestrian detection method provided in the first embodiment of the present application, this embodiment can be applied to the scene of pedestrian detection, the method can be executed by a pedestrian detection device, the device can be implemented in hardware and / or software, and Generally, it can be integrated into electronic devices such as computers with data computing capabilities, and specifically includes the following steps:
[0026] Step 101: Acquire image data to be detected, and use multiple convolution kernels to perform feature extraction on the image data to be detected to obtain multiple feature maps.
[0027] In this step, the image data to be detected may be image data captured by a vehicle-mounted camera, and the captured image data may be video data or photo data. If it is video data, each frame of data in the video data may be used as this The image data to be detected in the step; if it is photo data, it can be...
Embodiment 2
[0047] see figure 2 , figure 2 It is a schematic flow chart of obtaining gradient histogram features provided by Embodiment 2 of the present application, specifically including the following steps:
[0048] Step 201, for any feature map, normalize the feature map, and calculate the gradient magnitude and gradient direction of each pixel in the feature map.
[0049] In this step, normalization can reduce the impact of image shadows and illumination changes on subsequent detection agents. Specifically, normalization can be performed in units of pixels, and the calculation formula for normalization can be I(x, y)=I(x, y) gamma , where gamma is usually 0.5.
[0050] In addition, when calculating the gradient magnitude and gradient direction of each pixel in the feature map, for any pixel, the gradient components of the pixel in the first preset direction and the second preset direction can be determined first; then based on the first The gradient component of a preset direct...
Embodiment 3
[0063] see image 3 , image 3 It is a schematic structural diagram of a pedestrian detection device provided in Embodiment 3 of the present application. The pedestrian detection device provided in the embodiment of the present application can execute the pedestrian detection method provided in any embodiment of the present application, and has corresponding functional modules and beneficial effects for executing the method. The device can be implemented in software and / or hardware, such as image 3 As shown, the pedestrian detection device specifically includes: a feature map extraction module 301 , a gradient histogram feature acquisition module 302 , and a classification module 303 .
[0064] Wherein, the feature map extraction module is used to obtain the image data to be detected, and utilize multiple convolution kernels to perform feature extraction on the image data to be detected to obtain multiple feature maps;
[0065] The gradient histogram feature acquisition mo...
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