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PCC Net: Perspective Crowd Counting via Spatial Convolutional Network

This is an official implementation of the paper "PCC net" (PCC Net: Perspective Crowd Counting via Spatial Convolutional Network).

PCC Net.

In the paper, the experiments are conducted on the three populuar datasets: Shanghai Tech, UCF_CC_50 and WorldExpo'10. To be specific, Shanghai Tech Part B contains crowd images with the same resolution. For easier data prepareation, we only release the pre-trained model on ShanghaiTech Part B dataset in this repo.

Bracnhes

  1. ori_pt0.2_py2: the original version.
  2. ori_pt1_py3: the current version.
  3. vgg_pt1_py3: vgg-backbone PCC Net (higher performance).

Requirements

Data preparation

  1. Download the original ShanghaiTech Dataset [Link: Dropbox / BaiduNetdisk]
  2. Resize the images and the locations of key points.
  3. Generate the density maps by using the code.
  4. Generate the segmentation maps.

We also provide the processed Part B dataset for training. [Link]

Training model

  1. Run the train_lr.py: python train_lr.py.
  2. See the training outputs: Tensorboard --logdir=exp --port=6006.

In the experiments, training and tesing 800 epoches take 21 hours on GTX 1080Ti.

Expermental results

Quantitative results

We show the Tensorboard visualization results as below: Detialed infomation during the traning phase. The mae and mse are the results on test set. Others are triaining loss.

Visualization results

Visualization results on the test set as below: Visualization results on the test set. Column 1: input image; Column 2: density map GT; Column 3: density map prediction; Column 4: segmentation map GT; Column 5: segmentation map prediction.

Citation

If you use the code, please cite the following paper: