Derekduke / Pedestrian_Crossing_Intention_Prediction

Pedestrian Crossing Intention Prediction with Feature Fusion and Spatio-Temporal Attention.

Pedestrian Crossing Intention Prediction

Notification

Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention.

Paper in ArXiv: (to do) (submitted to IROS 2021)

This work improves the existing pedestrian crossing intention prediction method and achieves the latest state-of-the-art performance.

This work is heavily relied on the pedestrian action prediction benchmark: Kotseruba, Iuliia, Amir Rasouli, and John K. Tsotsos. "Benchmark for Evaluating Pedestrian Action Prediction." In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1258-1268, 2021.

Environment

python = 3.8
tensorflow-gpu = 2.2
numpy, opencv, PIL, matplotlib, etc
CPU:i7-6700K, GPU:RTX-2070super

Dataset Preparation

Download the JAAD Annotation and put JAAD file to this project's root directory (as ./JAAD).

Download the JAAD Dataset, and then put the video file JAAD_clips into ./JAAD (as ./JAAD/JAAD_clips).

Copy jaad_data.py from the corresponding repositories into this project's root directory (as ./jaad_data.py).

In order to use the data, first, the video clips should be converted into images. This can be done using script ./JAAD/split_clips_to_frames.sh following JAAD dataset's instruction.

Above operation will create a folder called images and save the extracted images grouped by corresponding video ids in the ./JAAD/images folder.

./JAAD/images/video_0001/
				00000.png
				00001.png
				...
./JAAD/images/video_0002/
				00000.png
				00001.png
				...		
...

Training

Note: our model extracts the semantic mask via DeeplabV3 (you need download pretrained segmentation model deeplabv3 before training and put checkpoint file into this project's root directory (as ./deeplabv3_mnv2_cityscapes_train_2018_02_05.tar.gz) so that the model can obtain the input semantic data).

Use train_test.py script with config_file:

python train_test.py -c <config_file>

All config_files are saved in ./config_files and you can review all offered model configs in ./config_files/config_list.yaml and all offered model architectures in ./model_imgs corresponding to configs.

For example, to train MASK-PCPA model run:

python train_test.py -c config_files/ours/MASK_PCPA_jaad_2d.yaml

The script will automatially save the trained model weights, configuration file and evaluation results in models/<dataset>/<model_name>/<current_date>/ folder.

See comments in the configs_default.yaml and action_predict.py for parameter descriptions.

Model-specific YAML files contain experiment options exp_opts that overwrite options in configs_default.yaml.

Test saved model

To re-run test on the saved model use:

python test_model.py <saved_files_path>

For example:

python test_model.py models/jaad/MASK_PCPA/xxxx/

You can download our pretrained model Google Drive (to do) for testing.

TODO Lists

• Readme Completion
• Pretrained Model
• Support PIE Dataset