https://github.com/brucezoom/ei339-projectmot
https://github.com/brucezoom/ei339-projectmot
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/brucezoom/ei339-projectmot
- Owner: BruceZoom
- License: gpl-3.0
- Created: 2019-11-24T14:46:11.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-29T14:54:45.000Z (over 6 years ago)
- Last Synced: 2025-02-02T15:33:38.765Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 4.74 MB
- Stars: 2
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# EI339-ProjectMOT
This is the repository for our work on the final project of EI339: Artificial Intelligence in Fall 2019.
## Structure
- `code`: The directory for all the source files
- `report.pdf`: The project report
- `code-explained.pdf`: A grading-friendly document that describes implementation of our ideas and modifications to open-source code.
## Dependencies
Following libraries are required to run our code.
- numpy
- scikit-learn
- opencv-python
- tensorflow >=1.0, <2.0
- torch >=1.3
- matplotlib
- pycocotools
- tqdm
- Pillow
Note that Tensorflow 2 is incompatible with the code for feature descriptor extraction, which is required for the real-time tracker.
Run the following command to install dependencies.
```bash
pip install -r code/requirements.txt
```
## Additional Resources
There are other resources you need to download to run our code.
- Download the weights for DeepSORT in 'resource.tar' from [JBox](https://jbox.sjtu.edu.cn/l/VooidI) and untar it as `code/resources`
- Download the weights for YOLOv3 also from [JBox](https://jbox.sjtu.edu.cn/l/VooidI) and place it under `code/yolov3/weights`
## Usage
To evaluate our implementation of Deep SORT with extended state space, download MOT16 dataset and run `code/deep_sort_app.py` according to the instructions in `code/README.md`. Assuming that the current working directory is `code`, here is an example:
```bash
python3 deep_sort_app.py \
--sequence_dir=MOT16/train/MOT16-02 \
--detection_file=resources/detections/MOT16_POI_train/MOT16-02.npy \
--min_confidence 0.3 --nn_budget 100 --display True
```
To test the real-time tracker, assuming that the current working directory is `code` and your camera is available, run
```bash
python3 real_time_app.py
```
A window will display the tracking result of the camera input for 30 seconds.