pedestrian-detection-adas-0002#
Use Case and High-Level Description#
Pedestrian detection network based on SSD framework with tuned MobileNet v1 as a feature extractor.
Example#
Specification#
Metric |
Value |
---|---|
Average Precision (AP) |
88% |
Target pedestrian size |
60 x 120 pixels on Full HD image |
Max objects to detect |
200 |
GFlops |
2.836 |
MParams |
1.165 |
Source framework |
Caffe* |
Average Precision metric described in: Mark Everingham et al. The PASCAL Visual Object Classes (VOC) Challenge.
Tested on an internal dataset with 1001 pedestrian to detect.
Inputs#
Image, name: data
, shape: 1, 3, 384, 672
in the format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order is BGR
.
Outputs#
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 - pedestrian)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
Demo usage#
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
Legal Information#
[*] Other names and brands may be claimed as the property of others.