# MOC-Detector

**Repository Path**: huogenwang/MOC-Detector

## Basic Information

- **Project Name**: MOC-Detector
- **Description**: [ECCV 2020] Actions as Moving Points
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-22
- **Last Updated**: 2021-09-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Actions as Moving Points

> Pytorch implementation of [Actions as Moving Points](https://arxiv.org/abs/2001.04608) (ECCV 2020).
>
>  View each action instance as a trajectory of moving points.

 Visualization results on validation set. *(GIFs will take a few minutes to load......)*

<div align="center" style="width:image width px;">
  <img  src="image/instance_SalsaSpin.gif" width=240>
  <img  src="image/instance_TrampolineJumping.gif" width=240>
  <img  src="image/diving.gif" width=240 >
</div>
<div align="center" style="width:image width px;">
  <img  src="image/run.gif" width=240>
  <img  src="image/soccer.gif" width=240>
  <img  src="image/instance_Fencing.gif" width=240 >
</div>



*(Note that the relative low scores are due to the property of the focal loss.)*

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## News & Updates

Jul. 08, 2020 -    First release of codes.

Jul. 24, 2020 -    Update ucf-pretrained JHMDB model and speed test codes.

Aug. 02, 2020 - Update visualization codes. Extract frames from a video and get the detection result (like above gifs).

Aug. 17, 2020 - Now our visualization supports instance level detection results (reflects video mAP).

Aug. 23, 2020 - We upload MOC with ResNet-18 in [Backbone](readme/Backbone.md).

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## MOC Detector Overview  

&emsp; We present a new action tubelet detection framework, termed as **MovingCenter Detector (MOC-detector)**, by treating an action instance as a trajectory of moving points. MOC-detector is decomposed into three crucial head branches:

- (1) **Center Branch** for instance center detection and action recognition.
- (2) **Movement Branch** for movement estimation at adjacent frames to form moving point trajectories.
- (3) **Box Branch** for spatial extent detection by directly regressing bounding box size at the estimated center point of each frame.

<div align="center" style="width:image width px;">
  <img  src="image/pipeline.png" width=420>
  <img  src="image/moving1.gif" width=400>
</div>

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## MOC-Detector Usage

### 1. Installation

Please refer to [Installation.md](readme/Installation.md) for installation instructions.

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### 2. Dataset

Please refer to [Dataset.md](readme/Dataset.md) for dataset setup instructions.

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### 3. Evaluation

You can follow the instructions in [Evaluation.md](readme/Evaluation.md) to evaluate our model and reproduce the results in original paper.

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### 4. Train

You can follow the instructions in [Train.md](readme/Train.md) to train our models.

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### 5. Visualization

You can follow the instructions in [Visualization.md](readme/Visualization.md) to get visualization results.

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## References

- Data augmentation codes from [ACT](https://github.com/vkalogeiton/caffe/tree/act-detector).

- Evaluation codes from [ACT](https://github.com/vkalogeiton/caffe/tree/act-detector).

- DLA-34 backbone codes from [CenterNet](https://github.com/xingyizhou/CenterNet).

  [ACT LICENSE](https://github.com/vkalogeiton/caffe/blob/act-detector/LICENSE)

  [CenterNet LICENSE](https://github.com/xingyizhou/CenterNet/blob/master/LICENSE)

  See more in [NOTICE](NOTICE)

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### Citation
If you find this code is useful in your research, please cite:

```bibtex
@InProceedings{li2020actions,
    title={Actions as Moving Points},
    author={Yixuan Li and Zixu Wang and Limin Wang and Gangshan Wu},
    booktitle={arXiv preprint arXiv:2001.04608},
    year={2020}
}
```