Multi Vehicle tracking in carla simulator

This repo host a comprehensive solution for multi-object tracking in combination with YOLO and DeepSORT in carla simulator . YOLOV8 model is trained on Carla dataset , which is avaiable in kaggle (https://www.kaggle.com/datasets/alechantson/carladataset)

Following software are required before installing required packages

1. Carla simulator : Download Carla simulator and follow the instuction guide - https://carla.readthedocs.io/en/latest/start_quickstart/

2. CUDA : Download and install CUDA - https://developer.nvidia.com/cuda-downloads

3. CuDnn : Install Cuda DNN - https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

4. Anacoda : https://www.anaconda.com/download

5. PyMOT : Download / clone this repo for evaluations - https://github.com/Videmo/pymot

6. Create and activate a new virtural conda env

   conda create --name <env-name> python=3.8
   conda activate <env-name> 
       NOTE : <env-name> should be the name of your virtural env
   

7. Install required packages

   pip install  requirements.txt
   

8. Once the setup is done Run Calra.exe file to launch simulator

9. One commapnd prompt or launch jupyter from conda prompt

10. Run track.ipynb file in jupyter

11. Run gt_deepsort.ipynv file followed by evaluate.ipynb to generate MOTA and MOTP values NOTE :

step to check PyTorch compatibility and install an appropriate version based on their CUDA and cuDNN configurations from this link , scroll down and you will see an option to select your system configs and tis will generate PIP INSTALL for CUDnn+torch which is compatable https://pytorch.org/

Eg : pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117