pedestrian-and-vehicle-detector-adas-0001

Use Case and High-Level Description

Pedestrian and vehicle detection network based on MobileNet v1.0 + SSD.

Example

Specification

Metric	Value
AP for pedestrians	88%
AP for vehicles	90%
Target pedestrian size	60x120 pixels
Target vehicle size	40x30 pixels
GFLOPS	3.974
MParams	1.650
Source framework	Caffe*

Average Precision (AP) metric is described in: Mark Everingham et al. “The PASCAL Visual Object Classes (VOC) Challenge”.

Tested on challenging internal datasets with 1001 pedestrian and 12585 vehicles to detect.

Performance

Inputs

name: "input" , shape: [1x3x384x672] - An input image in the format [BxCxHxW], where:
- B - batch size
- C - number of channels
- H - image height
- W - image width. Expected color order is BGR.

Outputs

The net outputs blob with shape: [1, 1, N, 7], where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max]
- image_id - ID of the image in the batch
- label - predicted class ID
- 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.

Legal Information

[*] Other names and brands may be claimed as the property of others.