Introduction

Based on AB3DMOT(https://github.com/xinshuoweng/AB3DMOT.git), apply the 128 lidar data from Deecamp 2020 and achieve the object tracking and visualization.

Dependencies

This code has been tested on python 2.7 and 3.6, and also requires the following packages: 1. scikit-learn==0.19.2 2. filterpy==1.4.5 3. numba==0.43.1 4. matplotlib==2.2.3 5. pillow==6.2.2 6. opencv-python==3.4.3.18 7. glob2==0.6 8. llvmlite==0.32.1 (for python 3.6) or llvmlite==0.31.0 (for python 2.7)

One can either use the system python or create a virtual enviroment (virtualenv for python2, venv for python3) specifically for this project (https://www.pythonforbeginners.com/basics/how-to-use-python-virtualenv). To install required dependencies on the system python, please run the following command at the root of this code:

$ pip install -r requirements.txt

To install required dependencies on the virtual environment of the python (e.g., virtualenv for python2), please run the following command at the root of this code:

$ pip install virtualenv
$ virtualenv .
$ source bin/activate
$ pip install -r requirements.txt

Dataset

The lidar data format is could see in the folder predictions

Deeacmp_AB3DMOT
├── data
│   ├── KITTI
│   │   │──prediction (your lidar data)
├── alfred
├── evaluation
├── results

If you want to visualize your lidar detection result, please change your lidar path

def load_bin(seq_name):
    file_name = seq_name + ".bin"
    v_f = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                     "yourfilepath/to/lidar(.bin)", file_name)
    pcs = load_pc_from_file(v_f)
    return pcs

Visualization

$ python main.py prediction

Acknowledgement

Part of the code is borrowed from "SORT"(https://github.com/abewley/sort)