person-vehicle-bike-detection-crossroad-0078#
Use Case and High-Level Description#
Person/Vehicle/Bike detector is based on SSD detection architecture, RMNet backbone, and learnable image downscale block (like person-vehicle-bike-detection-crossroad-0066, but with extra pooling). The model is intended for security surveillance applications and works in a variety of scenes and weather/lighting conditions.
Example#
Specification#
Metric |
Value |
---|---|
Mean Average Precision (mAP) |
65.12% |
AP people |
77.47% |
AP vehicles |
74.94% |
AP bikes |
44.14% |
Max objects to detect |
200 |
GFlops |
3.964 |
MParams |
1.178 |
Source framework |
Caffe* |
Average Precision (AP) is defined as an area under the precision/recall curve.
Validation dataset consists of 34,757 images from various scenes and includes:
Type of object |
Number of bounding boxes |
---|---|
Vehicle |
229,503 |
Pedestrian |
240,009 |
Bike |
62,643 |
Similarly, training dataset has 160,297 images with:
Type of object |
Number of bounding boxes |
---|---|
Vehicle |
501,548 |
Pedestrian |
706,786 |
Bike |
55,692 |
Inputs#
Image, name: data
, shape: 1, 3, 1024, 1024
in the format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
The 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- person, 2 - vehicle, 3 - bike)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.