This is a pedestrian detector for the Retail scenario. It is based on MobileNetV2-like backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block. The single SSD head from 1/16 scale feature map has 12 clustered prior boxes.
|Pose coverage||Standing upright, parallel to image plane|
|Support of occluded pedestrians||YES|
|Min pedestrian height||100 pixels (on 1080p)|
Average Precision (AP) is defined as an area under the precision/recall curve.
1, 3, 320, 544 in the format
B, C, H, W, where:
B- batch size
C- number of channels
H- image height
W- image width
Expected color order is
The net outputs blob with shape:
1, 1, 200, 7 in the format
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 (1 - person)
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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