/mtmc-vt

MTMC Vehicle Tracking For AI City challenge 2019

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

AI City Challenge 2019 Track1 MTMC Task

The code is for AI City Challenge 2019 Track1, MTMC Vehicle Tracking.

And we got the second place.

paper

Maintainers

Peilun Li, Guozhen Li, Meiqi Lu, Zhangxi Yan, Youzeng Li

Preparation

Dateset download: Track1-download,Size: 16.2GB

For running code correctly, the data should be put as follows:

├─ aic19-track1-mtmc
│  ├─ train
│  │  ├─ S01
│  │  │  ├─ c001
│  │  │  │  ├─ det
│  │  │  │  ├─ gt
│  │  │  │  ├─ mtsc
│  │  │  │  ├─ segm
│  │  │  │  ├─ calibration.txt
│  │  │  │  ├─ roi.jpg
│  │  │  │  ├─ det_reid_features.txt
│  │  │  │  ├─ vdo.avi
│  │  │  ├─ c002
│  │  │  ├─ c003
│  │  │  ├─ c004
│  │  │  ├─ c005
│  │  ├─ S03
│  │  ├─ S04
│  ├─ test
│  │  ├─ S02
│  │  ├─ S05
│  └─ cam_timestamp

Note that the det_reid_features.txt is the middle result of 1b_merge_visual_feature_with_ other_feature.py, and the other files are provided by organisers.

Step by Step for MTMC Vehicle Tracking

running code orderly

1_crop_vehicle_img_from_vdo.py

For each bounding box, crop the vehicle image and calculate the gps, according to the results of detection.

input:

  • input_dir:./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test

output:

  • for each video, produce det_gps_feature.txt to save gps information
  • for each video, save all cropped image.

1a_extract_visual_feature_for_each_img.py

  • extract reid feature for each corpped image, the train and inference pipeline follows reid-baseline

1b_merge_visual_feature_with_other_feature.py

Merge reid feature and gps information into one file.

input:

  • input_dir: ./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test
  • gps information file /det_gps_feature.txt, from 1
  • ReID feature file /deep_features.txt, from 1a

output:

  • for each video, produce det_reid_features.txt file

2_tracking.py

multi targets tracking for each video.

input:

  • input_dir: ./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test
  • ID file already_used_number.txt, avoid reusing number
  • /det_reid_features.txt from 1b

output:

  • for each video, produce tracking result file det_reid_track.txt

2a_post_process_for_tracking.py

Optimize tracking result to solve target lost.

input:

  • input_dir: ./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test
  • fps file train_fps.txt
  • for each video, need det_reid_track.txt from 2

output:

  • for each video, produce tracking result optimized_track.txt

2b_remove_overlap_boxes.py

Remove overlapped bounding box.

input:

  • input_dir: ./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test
  • for each video, need optimized_track.txt from 2a

output:

  • for each video, produce tracking result optimized_track_no_overlapped.txt

3a_track_based_reid.py

Calculate reid similarity between tracks.

input:

  • input_dir: ./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test
  • for each video, need optimized_track_no_overlapped.txt from 2b

output:

  • ReID similarity file ranked

3b_trajectory_processing.py

Calculate the gps-trajectory cohesion between tracks, should run the code trajectory_processing/main.py

input:

  • input_dir: ./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test
  • for each video, need optimized_track_no_overlapped.txt from 2b

output:

  • gps-trajectroy file gps_and_time_new

4a_match_tracks_for_crossroad.py

MTMC tracking for crossroad scene

input:

  • input_dir: ./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test
  • for each video, need optimized_track_no_overlapped.txt from 2b
  • ranked from 3a
  • gps_and_time_new from 3b

output:

  • match result submission_crossroad_train

4b_match_tracks_for_arterialroad.py

MTMC tracking for arterial road scene

input:

  • input_dir: ./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test
  • for each video, need optimized_track_no_overlapped.txt from 2b
  • ranked from 3a
  • gps_and_time_new from 3b

output:

  • match result submission_normal_train

5a_merge_results.py

merge the results from different scenes

input:

  • submission_crossroad_train from 4a
  • submission_normal_train from 4b

output:

  • merged result file submission

5b_adapt_boxes.py

post process for each bounding box

input:

  • input_dir: ./aic19-track1-mtmc/trainor./aic19-track1-mtmc/test
  • submission from 5a

output:

  • result file submission_adpt

5c_convert_to_submission.py

convert the result to submission format

input:

  • submission_adpt from 5b

output:

  • submission file track1.txt

Guide for use

Run the code from 1_\*.py to 5c_\*.py orderly. The train and inference for ReID follows reid-baseline

We propose starting with 2_tracking.py, if you are the first time to this project. And we provide the results of 1b. You could download it, put them in the right place as metioned above and rename them as det_reid_features.txt.

Extras

result of 1b