https://github.com/sustainablemobility/cyc-cp

This repository provides the benchmark for Cycling Close Pass Near Miss (Cyc-CP).
https://github.com/sustainablemobility/cyc-cp

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This repository provides the benchmark for Cycling Close Pass Near Miss (Cyc-CP).

• Host: GitHub
• URL: https://github.com/sustainablemobility/cyc-cp
• Owner: SustainableMobility
• Created: 2023-04-12T01:17:31.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-04-27T07:57:45.000Z (over 1 year ago)
• Last Synced: 2024-11-11T01:28:21.745Z (2 months ago)
• Language: Jupyter Notebook
• Size: 9.13 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# cyc-cp

This repository provides the benchmark for Cycling Close Pass Near Miss  (Cyc-CP).

## Installation

1. Clone the repo: `git clone https://github.com/SustainableMobility/cyc-cp.git`

2. Install the package: 

    1. `cd cyc-cp`

    2. `pip install -e .`

## How To Use

1. `cd cyc-cp`

2. Train crnn or i3d model following:

    1. `python ./cnm/scene_lvl/i3d/i3d.py --csv_data_path path/to/csv_data --image_data_path path/to/image_data --exp_data_dir path/to/save/exp_data`   

    2. `python ./cnm/scene_lvl/crnn/crnn.py --csv_data_path path/to/csv_data --image_data_path path/to/image_data --exp_data_dir path/to/save/exp_data` 

    * where the meaning of the arguments can be found in the code help. Specifically, 

        * --csv_data_path: The file path of the .csv file with dataset info.

        * --image_data_path: The directory path saving all video frames.

        * --exp_data_dir: The directory to save results to.

## Dataset Preparation

* Victorian On-bike Cycling (legacy): available on [Monash Bridges](https://figshare.com/projects/A_Benchmark_for_Cycling_Close_Pass_Near_Miss_Event_Detection_from_Video_Streams/163438)

* Victorian On-bike Cycling (ongoing): under collection ...

* CARLA (simulation): available on [Monash Bridges](https://figshare.com/projects/A_Benchmark_for_Cycling_Close_Pass_Near_Miss_Event_Detection_from_Video_Streams/163438)

* NuScences: available on 

## Hardware Requirements Summary

* Disk: to save all datasets about > 2TB disk space is required.

* RAM and GPU: (only tested on Victorian On-bike Cycling (legacy))

    * Scene-level:

        * I3D: (Batch_size:16, image_size: 256x342, frames: [-5, 15])

            * GPU memory: 7.5GB, CPU memory: 3 GB (RTX-2080 has 8 GB memory, so that’s why [-5, 15] frames are included in a video clip.)

        * CRNN: (Batch_size: 16, image_size: 224x224, frame: [20,25])

            * GPU memory: 3 GB, CPU memory: 6 GB

    * Instance-level (**TODO**)

More Notes about the project can be found in [the shared google doc](https://docs.google.com/document/d/13UxCa-qcIuyZ6V3-oTSfUFmnSvWxZjIalBQ6MdcVUCs/edit?usp=sharing).