Multimodal Language Vehicle Retrieval (MLVR)

The code is for our paper in the 7th AI City Challenge Track 2, Tracked-Vehicle Retrieval by Natural Language Descriptions, reaching the 2nd rank on the public leaderboard.

A Unified Multi-modal Structure for Retrieving Tracked Vehicles through Natural Language Descriptions

Introduction

Through the development of multi-modal and contrastive learning, image and video retrieval have made immense progress over the last years. Organically fused text, image, and video knowledge brings huge potential opportunities for multi-dimension, and multi-view retrieval, especially in traffic senses. This paper proposes a novel Multi-modal Language Vehicle Retrieval (MLVR) system, for retrieving the trajectory of tracked vehicles based on natural language descriptions. The MLVR system is mainly combined with an end-to-end text-video contrastive learning model, a CLIP few-shot domain adaption method, and a semi-centralized control optimization system. Through a comprehensive understanding the knowledge from the vehicle type, color, maneuver, and surrounding environment, the MLVR forms a robust method to recognize an effective trajectory with provided natural language descriptions. Under this structure, our approach has achieved 81.79% Mean Reciprocal Rank (MRR) accuracy on the test dataset, in the 7th AI City Challenge Track 2, Tracked-Vehicle Retrieval by Natural Language Descriptions, rendering the 2nd rank on the public leaderboard.

Requirements

pip install -r requirements.txt

Structure

MLVR
├── data                   # put aicity2023 track 2 data
├── docs                   # pictures and paper
├── preprocessing          # process the data for model
├── model                  # modules for MLVR                 
│   ├── vrm                # Video Recognition Module
│   ├── vct                # Vehicle Color and Type Modules
│   ├── vmm                # Vehicle Motion Module
│   └── vsm                # Vehicle Surrounding Module
├── postprocessing         # Model Postprocessing
│   ├── matrix             # vrm, vct, vmm, vsm score matrices
│   └── final_results.json # submit result 81.79%
├── requirements.txt
└── README.md

Running

Preprocessing

1. Get images from the video

cd ./preprocessing
python extract_vdo_frms.py

2. Get background of the images

python generate_median.py

3. Generate the video clip for video recognition module

python create_video_clip.py

4. Format the text input for video recognition module

python create_vrm_data.py

5. Crop the vehicle images for vehicle color and type modules

python crop_vehicle_bbox.py

6. Format the text input for vehicle color and type modules

python create_vct_data.py

Model

1. Video Recognition Module (baseline)

   This part is modified from X-CLIP.

   Please download the pretrain model here for test, and put it in \model\vrm\ckpts\.

cd ./model/vrm
sh ./scripts/train.sh # train
sh ./scripts/test.sh  # test

2. Vehicle Color and Type Modules

   This part is modified from Tip-Adapter.

cd ./model/vct
python train.py --config vehicle_color_train.yaml  # vehicle color module train
python test.py --config vehicle_color_test.yaml  #  vehicle color module test

python train.py --config vehicle_type_train.yaml  # vehicle type module train
python test.py --config vehicle_type_test.yaml  #  vehicle type module test

3. Vehicle Motion Modules

cd ./model/vmm
python main.py # vehicle color module

4. Vehicle Surrounding Modules

   This part is modified from GLIP.

cd ./model/vsm/branch1
python vsm1.py # vehicle surrounding module branch 1

cd ./model/vsm/branch2
python get_candidates.py # vehicle surrounding module branch 2

Postprocessing

Run the following command to generate the final submit result 81.79%.

cd ./postprocessing
python mcs.py # match control system