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pedestrian-detection-adas-0002.md

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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

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. 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 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.