Immortal tracker

Related tags

Deep Learning ImmortalTracker
Overview

Immortal_tracker

Prerequisite

Our code is tested for Python 3.6.
To install required liabraries:

pip install -r requirements.txt

Waymo Open Dataset

Prepare dataset & off-the-shelf detections

Download WOD perception dataset:

#Waymo Dataset         
└── waymo
       ├── training (not required)  
       ├── validation   
       ├── testing

To extract timestamp infos/ego infos from .tfrecord files, run the following:

bash preparedata/waymo/waymo_preparedata.sh  /
   
    /waymo

Run the following to convert detection results into to .npz files. The detection results should be in official WOD submission format(.bin)
We recommand you to use CenterPoint(two-frame model for tracking) detection results for reproducing our results. Please follow https://github.com/tianweiy/CenterPoint or email its author for CenterPoint detection results.

bash preparedata/waymo/waymo_convert_detection.sh 
   
    /detection_result.bin cp

#you can also use other detections:
#bash preparedata/waymo/waymo_convert_detection.sh

Inference

Use the following command to start inferencing on WOD. The validation set is used by default.

python main_waymo.py --name immortal --det_name cp --config_path configs/waymo_configs/immortal.yaml --process 8

Evaluation with WOD official devkit:

Follow https://github.com/waymo-research/waymo-open-dataset to build the evaluation tools and run the following command for evaluation:

#Convert the tracking results into .bin file
python evaluation/waymo/pred_bin.py --name immortal
#For evaluation

   
    /bazel-bin/waymo_open_dataset/metrics/tools/compute_tracking_metrics_main mot_results/waymo/validation/immortal/bin/pred.bin 
    
     /validation_gt.bin

nuScenes Dataset

Prepare dataset & off-the-shelf detections

Download nuScenes perception dataset

# For nuScenes Dataset         
└── NUSCENES_DATASET_ROOT
       ├── samples       
       ├── sweeps       
       ├── maps         
       ├── v1.0-trainval 
       ├── v1.0-test

To extract timestamp infos/ego infos, run the following:

bash preparedata/nuscenes/nu_preparedata.sh 
   
    /nuscenes

Run the following to convert detection results into to .npz files. The detection results should be in official nuScenes submission format(.json)
We recommand you to use centerpoint(two-frame model for tracking) detection results for reproducing our results.

bash preparedata/nuscenes/nu_convert_detection.sh  
   
    /detection_result.json cp

#you can also use other detections:
#bash preparedata/nuscenes/nu_convert_detection.sh

Inference

Use the following command to start inferencing on nuScenes. The validation set is used by default.

python main_nuscenes.py --name immortal --det_name cp --config_path configs/nu_configs/immortal.yaml --process 8

Evaluation with nuScenes official devkit:

Follow https://github.com/nutonomy/nuscenes-devkit to build the official evaluation tools for nuScenes. Run the following command for evaluation:

/nuscenes "> 
#To convert tracking results into .json format
bash evaluation/nuscenes/pipeline.sh immortal
#To evaluate
python 
   
    /nuscenes-devkit/python-sdk/nuscenes/eval/tracking/evaluate.py \
"./mot_results/nuscenes/validation_2hz/immortal/results/results.json" \
--output_dir "./mot_results/nuscenes/validation_2hz/immortal/results" \
--eval_set "val" \
--dataroot 
    
     /nuscenes
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Comments
  • Disadvantages of simply increase MaxAge?

    Disadvantages of simply increase MaxAge?

    Hi, I'm interested in your work, but I have some questions I want to discuss with you.

    As oc-sort said, the noise of kalman filter will increase with t^2 as long as there is no observations in update step, which makes the prediction of kalman filter not reliable anymore. So in my experiments in MOT17 dataset, simply increase MaxAge of lost trajectories will cause more wrong matches.

    What do you think of this question? Looking forward to your reply!

    opened by HanGuangXin 2
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