Detection in Crowded Scenes

Input

(Image from http://www.crowdhuman.org/)

Output

Usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 crowd_det.py

If you want to specify the input image, put the image path after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 crowd_det.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.

$ python3 crowd_det.py --video VIDEO_PATH

By adding the --model_type option, you can specify model type which is selected from "rcnn_fpn_baseline", "rcnn_emd_simple", "rcnn_emd_refine". (default is rcnn_fpn_baseline)

$ python3 crowd_det.py --model_type rcnn_fpn_baseline

Reference

• Detection in Crowded Scenes

Framework

Pytorch

Model Format

ONNX opset=16

Netron

rcnn_fpn_baseline_mge.onnx.prototxt
rcnn_emd_simple_mge.onnx.prototxt
rcnn_emd_refine_mge.onnx.prototxt