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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 size

  • C - number of channels

  • H - image height

  • W - 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 batch

  • label - 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:

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