This is a pedestrian detector based on backbone with hyper-feature + R-FCN for the Retail scenario.
|Pose coverage||Standing upright, parallel to image plane|
|Support of occluded pedestrians||YES|
|Min pedestrian height||80 pixels (on 1080p)|
|Max objects to detect||200|
Average Precision (AP) is defined as an area under the precision/recall curve. Validation dataset consists of ~50K of images from ~100 different scenes.
data, shape: [1x3x544x992] - An input image in following format [1xCxHxW]. The expected channel order is BGR.
im_info, shape: [1x6] - An image information [544, 992, 992/
The net outputs blob with shape: [1, 1, N, 7], where N is the number of detected bounding boxes. Each detection has the format [
image_id- ID of the image in the batch
label- predicted class ID
conf- confidence for the predicted class
y_min) - coordinates of the top left bounding box corner
y_max) - coordinates of the bottom right bounding box corner.
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