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AI City 2023: Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-based Vehicle Retrieval

The 3rd Place Solution to The 7th NVIDIA AI City Challenge (2023) Track 2: Tracked-Vehicle Retrieval by Natural Language Descriptions.

Scoreboard

The 3nd Place Submission to AICity Challenge 2023 Tracked-Vehicle Retrieval by Natural Language Descriptions

Rank Team ID Team name MRR Score
1 9 HCMIU-CVIP 0.8263
2 28 IOV 0.8179
3 85 AIO-NLRetrieve (Ours) 0.4795
4 151 AIO2022 0.4659
5 76 DUT_ReID 0.4392

Prepare

  • Preprocess the dataset to prepare frames, motion maps, video clips

scripts/extract_vdo_frms.py is a Python script that is used to extract frames.

scripts/get_motion_maps.py is a Python script that is used to get motion maps.

scripts/get_clip_maps.py is a Python script that is used to get video clips.

scripts/extract_clip_feature_tracks.ipynb is a Python script that is used to get clip features

  • You can download the necessary files here

The directory structures in data is as follows

data/
└── AIC23_Track2_NL_Retrieval/
    └── data/
        ├── clip_feats/
        ├── data/
        │   ├── bk_map/
        │   ├── motion_heatmap/
        │   └── motion_map_iou/
        ├── train/
        │   ├── S01/
        │   ├── S03/
        │   ├── S04/
        ├── validation/
        │   ├── S02/
        │   ├── S05/
        ├── train-tracks_nlpaug.json    
        ├── train-tracks_nlpaug_2.json  
        ├── train-tracks_nlpaug_3.json
        └── ...

Train

The configuration files are in configs and train different models by (set up the right data path first):

bash run/single_baseline_aug1.sh
bash run/single_baseline_aug1_plus.sh
bash run/single_baseline_aug2.sh
bash run/circle_loss.sh
bash run/view_triplet_hard.sh
bash run/dual_baseline_aug3.sh

Eval

You can also change the RESTORE_FROM in your configuration file to checkpoints, and load checkpoints to eval (download the checkpoints first). Take dual_baseline_aug1 as an example:

bash run/eval_only.sh

Ensemble

Change the RESTORE_FROM in your configuration file and extract the embeddings

In addition, acquire the car and text features used in short-distance modeling by running the following code:

  1. Run python3 scripts/get_location_info.py to generate location information for each camera, which will be used in our post-processing stage.

  2. Run python3 scripts/get_relation_info.py to generate relationship features for test tracks, which will be used in our post-processing stage.

base run/submit.py

Post processing

Copy the sim_mat.npy file generated from previous step and paste it to post_process_module/post_process/post-process-part1/sim_mat folder.

run the post_process.py files in 2 post procesing modules. See post_processing module for more details.

Citation

If you use this method or this code in your research, please cite as:

@InProceedings{Ngo_2023_CVPR,
    author    = {Ngo, Bach Hoang and Nguyen, Dat Thanh and Do-Tran, Nhat-Tuong and Thien, Phuc Pham Huy and An, Minh-Hung and Nguyen, Tuan-Ngoc and Hoang, Loi Nguyen and Nguyen, Vinh Dinh and Dinh, Vinh},
    title     = {Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-Based Vehicle Retrieval},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2023},
    pages     = {5409-5418}
}