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.
| Metric | Value |
|---|---|
| AP | 88.62% |
| Pose coverage | Standing upright, parallel to image plane |
| Support of occluded pedestrians | YES |
| Occlusion coverage | <50% |
| Min pedestrian height | 100 pixels (on 1080p) |
| GFlops | 2.300 |
| MParams | 0.723 |
| Source framework | Caffe* |
Average Precision (AP) is defined as an area under the precision/recall curve.
Image, name: data, shape: 1, 3, 320, 544 in the format B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
Expected color order is BGR.
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, label, conf, x_min, y_min, x_max, y_max], where:
image_id- ID of the image in the batchlabel- predicted class ID (1 - person)conf- confidence for the predicted class- (
x_min,y_min) - coordinates of the top left bounding box corner - (
x_max,y_max) - coordinates of the bottom right bounding box corner
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
- Multi Camera Multi Target Python* Demo
- Object Detection C++ Demo
- Object Detection Python* Demo
- Pedestrian Tracker C++ Demo
- Single Human Pose Estimation Demo
- Social Distance C++ Demo
[*] Other names and brands may be claimed as the property of others.