README.md

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 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, 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 batch
• label - 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:

• Object Detection C++ Demo
• Object Detection Python* Demo
• Pedestrian Tracker C++ Demo
• Single Human Pose Estimation Demo

Legal Information

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