| # GIST Multi-Camera Dataset | |
| Collect and Annotate 3D Multi-Camera Multi-Object Tracking Dataset | |
| # Prepare and print a checkerboard | |
| 1) Go to https://calib.io/ | |
| 2) Pattern Generator (1) Target Type: Checkerboard, | |
| 3) Board Width [mm]: 841, (3) Board Height [mm]: 1189, | |
| 4) Rows: 6, (5) Columns: 4, Checker Width [mm]: 195 | |
| 5) Download PDF | |
| 6) Print it two piceces of the PDF with A0 paper (841x1189 mm) | |
| 7) Glue them together precisely and tightly | |
| # Install Packages | |
| sudo apt install ffmpeg | |
| pip install numpy==1.23.5 pandas==1.5.1 opencv-python==4.10.0.84 | |
| # Process | |
| 0. Videos are captured from Android devices using [RecSync](https://github.com/MobileRoboticsSkoltech/RecSync-android.git) with some modifications. | |
| - Adding TrueTime dependency `'com.github.instacart:truetime-android:3.4'` to `app/build.gradle`. In `MainActivity.java` make sure to init TrueTime, `trueTimeInitialized.await();` | |
| - `CameraController.java`, `long synchronizedTimestampNs = TrueTime.now().getTime();`. This change adapts one captured frame cooresponding to a timestamp obtained from TrueTime. | |
| - `SoftwareSyncController.java`, `leaderAddress = InetAddress.getByName("172.17.3.136");` change IP address of leader server. | |
| - If all Android devices are not the same model, we need to move some statements out of `if (isLeader) {})` | |
| ``` | |
| exposureSeekBar.setVisibility(View.VISIBLE); | |
| sensitivitySeekBar.setVisibility(View.VISIBLE); | |
| exposureSeekBar.setOnSeekBarChangeListener( | |
| ``` | |
| This setting allows us to ajust `sensitivity` and `exposure` of Android camera. | |
| 1. Videos are extracted using FFMPEG | |
| 2. Frames of each video are matched using timestamp obtained from `TrueTime` | |
| 3. `calibration_syncvideos.py` outputs calibration matrix from synchronized and extracted frames. Synchronized frames are saved in a folder name as `./GMC0715/S20`. | |
| # Contact | |
| Linh Ma (linh.mavan@gm.gist.ac.kr), Machine Learning & Vision Laboratory, GIST, South Korea | |
| ### Citation | |
| If you find this project useful in your research, please consider citing by: | |
| ``` | |
| @article{linh2024inffus, | |
| title={Track Initialization and Re-Identification for {3D} Multi-View Multi-Object Tracking}, | |
| author={Linh Van Ma, Tran Thien Dat Nguyen, Ba-Ngu Vo, Hyunsung Jang, Moongu Jeon}, | |
| journal={Information Fusion}, | |
| volume = {111}, | |
| year={2024}, | |
| publisher={Elsevier} | |
| } | |
| ``` | |
| --- | |
| license: mit | |
| --- | |