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https://github.com/Dawn-LX/VidVRD-tracklets
Video Visual Relation Detection (VidVRD) tracklets generation. also for ACM MM Visual Relation Understanding Grand Challenge
https://github.com/Dawn-LX/VidVRD-tracklets
Last synced: 2 months ago
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Video Visual Relation Detection (VidVRD) tracklets generation. also for ACM MM Visual Relation Understanding Grand Challenge
- Host: GitHub
- URL: https://github.com/Dawn-LX/VidVRD-tracklets
- Owner: Dawn-LX
- Created: 2021-08-09T09:12:21.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-12-05T05:00:57.000Z (about 2 years ago)
- Last Synced: 2024-08-03T13:14:42.461Z (5 months ago)
- Language: Python
- Size: 3.38 MB
- Stars: 38
- Watchers: 2
- Forks: 9
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-video-object-detection - VidVRD - LX/VidVRD-tracklets?style=social"/> : "Video Relation Detection via Tracklet based Visual Transformer". (**[ACM 2021](https://arxiv.org/pdf/2108.08669.pdf)**) (Frameworks)
README
# Update
We proposed a new VidSGG framework: [Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs](https://arxiv.org/abs/2112.04222), which is accepted by CVPR2022. code is released [here](https://github.com/Dawn-LX/VidSGG-BIG).
# Update
We updated the code for taining MEGA on VidVRD-dataset, including following files:
- `MEGA/datasets/vidvrd-dataset/img_index`
- `MEGA/mega_core/data/datasets/vidvrd.py`
- `MEGA/tools/extract_coco_vidvrd.py`
See if these files can help in case you need to train the MEGA by yourself.
# VidOR-tracklets [here](https://drive.google.com/drive/folders/1wWkzHlhYcZPQR4fUMTTJEn2SVVnhGFch?usp=sharing)
We won the 1st place of Video Relation Understanding (VRU) Grand Challenge in ACM Multimedia 2021. The corresponding technical report: [here](https://dl.acm.org/doi/10.1145/3474085.3479231) or [arXiv version](https://arxiv.org/abs/2108.08669).
This repository contains codes for Video Visual Relation Detection (VidVRD) tracklets generation based on [MEGA](https://github.com/Scalsol/mega.pytorch) and [deepSORT](https://github.com/nwojke/deep_sort). These tracklets are also suitable for [ACM MM Visual Relation Understanding (VRU) Grand Challenge](https://videorelation.nextcenter.org/) (which is base on the [VidOR dataset](https://xdshang.github.io/docs/vidor.html)).
## **If you are only interested in the generated tracklets, you can ignore the code and download them directly from [here](https://drive.google.com/drive/folders/1wWkzHlhYcZPQR4fUMTTJEn2SVVnhGFch?usp=sharing)**
## Download generated tracklets directly
We release the object tracklets for [VidOR](https://xdshang.github.io/docs/vidor.html) train/validation/test set. You can download the tracklets [here](https://drive.google.com/drive/folders/1wWkzHlhYcZPQR4fUMTTJEn2SVVnhGFch?usp=sharing), and put them in the following folder as
```
├── deepSORT
│ ├── ...
│ ├── tracking_results
│ │ ├── VidORtrain_freq1_m60s0.3_part01
│ │ ├── ...
│ │ ├── VidORtrain_freq1_m60s0.3_part14
│ │ ├── VidORval_freq1_m60s0.3
│ │ ├── VidORtest_freq1_m60s0.3
│ │ ├── readme.md
│ │ └── format_demo.py
│ └── ...
├── MEGA
│ ├── ...
│ └── ...
```
Please refer to `deepSORT/tracking_results/readme.md` for more details
## Evaluate the tracklets mAP
Run `python deepSORT/eval_traj_mAP.py` to evaluate the tracklets mAP. (you might need to change some args in deepSORT/eval_traj_mAP.py)
## Generate object tracklets by yourself
The object tracklets generation pipeline mainly consists of two parts: ``MEGA`` (for video object detection), and ``deepSORT`` (for video object tracking).
### Quick Start
1. Install MEGA as the official instructions `MEGA/INSTALL.md` (Note that the folder path may be different when installing).
- If you have any trouble when installing MEGA, you can try to clone the [official MEGA repository](https://github.com/Scalsol/mega.pytorch) and install it, and then replace the official `mega.pytorch/mega_core` with our modified `MEGA/mega_core`. Refer to `MEGA/modification_details.md` for the details of our modifications.
2. Download the [VidOR dataset](https://xdshang.github.io/docs/vidor.html) and the pre-trained [weight](https://drive.google.com/file/d/1nypbyRLpiQkxr7jvnnM4LEx2ZJuzrjws/view?usp=sharing) of MEGA. Put them in the following folder as
```
├── deepSORT/
│ ├── ...
├── MEGA/
│ ├── ...
│ ├── datasets/
│ │ ├── COCOdataset/ # used for MEGA training
│ │ ├── COCOinVidOR/ # used for MEGA training
│ │ ├── vidor-dataset/
│ │ │ ├── annotation/
│ │ │ │ ├── training/
│ │ │ │ └── validation/
│ │ │ ├── img_index/
│ │ │ │ ├── VidORval_freq1_0024.txt
│ │ │ │ ├── ...
│ │ │ ├── val_frames/
│ │ │ │ ├── 0001_2793806282/
│ │ │ │ │ ├── 000000.JPEG
│ │ │ │ │ ├── ...
│ │ │ │ ├── ...
│ │ │ ├── val_videos/
│ │ │ │ ├── 0001/
│ │ │ │ │ ├── 2793806282.mp4
│ │ │ │ │ ├── ...
│ │ │ │ ├── ...
│ │ │ ├── train_frames/
│ │ │ ├── train_videos/
│ │ │ ├── test_frames/
│ │ │ ├── test_videos/
│ │ │ └── video2img_vidor.py
│ │ └── construct_img_idx.py
│ ├── training_dir/
│ │ ├── COCO34ORfreq32_4gpu/
│ │ │ ├── inference/
│ │ │ │ ├── VidORval_freq1_0024/
│ │ │ │ │ ├── predictions.pth
│ │ │ │ │ └── result.txt
│ │ │ │ ├── ...
│ │ │ └── model_0180000.pth
│ │ ├── ...
```
3. Run `python MEGA/datasets/vidor-dataset/video2img_vidor.py` (note that you may need to change some args) to extract frames from videos (This causes a lot of data redundancy, but we have to do this, because MEGA takes image data as input).
4. Run `python MEGA/datasets/construct_img_idx.py` (note that you may need to change some args) to generate the img_index used in MEGA inference.
- The generated `.txt` files will be saved in `MEGA/datasets/vidor-dataset/img_index/`. You can use `VidORval_freq1_0024.txt` as a demo for the following commands.
5. Run the following command to detect frame-level object proposals with bbox features (RoI pooled features).
```
CUDA_VISIBLE_DEVICES=0 python \
MEGA/tools/test_net.py \
--config-file MEGA/configs/MEGA/inference/VidORval_freq1_0024.yaml \
MODEL.WEIGHT MEGA/training_dir/COCO34ORfreq32_4gpu/model_0180000.pth \
OUTPUT_DIR MEGA/training_dir/COCO34ORfreq32_4gpu/inference
```
- The above command will generate a `predictions.pth` file for this `VidORval_freq1_0024` demo. We also release this `predictions.pth` [here](https://drive.google.com/drive/folders/1l0pcOGycs6fnmMQu2RLK8Vu_iD9TG2zO?usp=sharing).
- the config files for VidOR train set are in `MEGA/configs/MEGA/partxx`
- The `predictions.pth` contains frame-level box positions and features (RoI features) for each object. For RoI features, they can be accessed through `roifeats = boxlist.get_field("roi_feats")`, if you are familiar with MEGA or [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark)
6. Run `python MEGA/mega_boxfeatures/cvt_proposal_result.py` (note that you may need to change some args) to convert `predictions.pth` to a `.pkl` file for the following deepSORT stage.
- We also provide `VidORval_freq1_0024.pkl` [here](https://drive.google.com/file/d/1r9WZG6pXXk8IT7E1E8oqJ4zBl38uYiqq/view?usp=sharing)
6. Run `python deepSORT/deepSORT_tracking_v2.py` (note that you may need to change some args) to perform deepSORT tracking. The results will be saved in `deepSORT/tracking_results/`
## Train MEGA for VidOR by yourself
1. Download MS-COCO and put them as shown in above.
2. Run `python MEGA/tools/extract_coco.py` to extract annotations for COCO in VidOR, which results in `COCO_train_34classes.pkl` and `COCO_valmini_34classes.pkl`
3. train MEGA by the following commands:
```
python -m torch.distributed.launch \
--nproc_per_node=4 \
tools/train_net.py \
--master_port=$((RANDOM + 10000)) \
--config-file MEGA/configs/MEGA/vidor_R_101_C4_MEGA_1x_4gpu.yaml \
OUTPUT_DIR MEGA/training_dir/COCO34ORfreq32_4gpu
```
## Extract the bbox features based on the given bbox positions
- refer to `MEGA/tools/extract_gt_boxfeatures.py`
- **some notes**
The MEGA code is so fucking complex. When I extracting the bbox features, I just run MEGA inference to obtain the bbox positions and store the bbox features (RoI features) for each frame simultaneously.
Again, the MEGA code is so fucking complex, Although I wrote the code `extract_gt_boxfeatures.py`, I can not make sure the feature extracted based on given bbox are the same as stored simultaneously by MEGA inference. There are some random shuffle in the memory bank of MEGA.
If our work is helpful for your research, please cite our publication:
```
@inproceedings{gao2021classification,
title={Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs},
author={Gao, Kaifeng and Chen, Long and Niu, Yulei and Shao, Jian and Xiao, Jun},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
year={2022}
}
```