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person-vehicle-bike-detection-crossroad-1016

Use Case and High-Level Description

MobileNetV2 + SSD-based network is for Person/Vehicle/Bike detection in security surveillance applications. Works in a variety of scenes and weather/lighting conditions.

Example

Specification

Metric

Value

Mean Average Precision (mAP)

62.55%

AP people

73.63%

AP vehicles

77.84%

AP bikes

36.18%

Max objects to detect

200

GFlops

3.560

Source framework

PyTorch*

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

Non-vehicle

62,643

Similarly, training dataset has 219,181 images with:

Type of object

Number of bounding boxes

Vehicle

810,323

Pedestrian

1,114,799

Non-vehicle

62,334

Inputs

Image, name: input.1, shape: 1, 3, 512, 512 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order: 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 (0 - non-vehicle, 1 - vehicle, 2 - person)

  • 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:

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