# Action-Recognition

**Repository Path**: llssqq/Action-Recognition

## Basic Information

- **Project Name**: Action-Recognition
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-06-28
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Action Recognition in Video

This repo will serve as a playground where I investigate different approaches to solving the problem of action recognition in video.

I will mainly use the [UCF-101 dataset](https://www.crcv.ucf.edu/data/UCF101.php).

<p align="center">
    <img src="assets/crawling.gif" width="400"\>
</p>

## Setup

```
$ cd data/              
$ bash download_ucf101.sh     # Downloads the UCF-101 dataset (~7.2 GB)
$ unrar x UCF101.rar          # Unrars dataset
$ unzip ucfTrainTestlist.zip  # Unzip train / test split
$ python3 extract_frames.py   # Extracts frames from the video (~26.2 GB, go grab a coffee for this)
```

## ConvLSTM

The only approach investigated so far. Enables action recognition in video by a bi-directional LSTM operating on frame embeddings extracted by a pre-trained ResNet-152 (ImageNet).

The model is composed of:
* A convolutional feature extractor (ResNet-152) which provides a latent representation of video frames
* A bi-directional LSTM classifier which based on the latent representation of the video predicts the activity depicted

I have made a trained model available [here](https://drive.google.com/open?id=1GlpN0m9uLbI9dg1ARbW9hDEf-VWe4Asl).

### Train  

```
$ python3 train.py  --dataset_path data/UCF-101-frames/ \
                    --split_path data/ucfTrainTestlist \
                    --num_epochs 200 \
                    --sequence_length 40 \
                    --img_dim 112 \
                    --latent_dim 512
```

### Test on Video

```
$ python3 test_on_video.py  --video_path data/UCF-101/SoccerPenalty/v_SoccerPenalty_g01_c01.avi \
                            --checkpoint_model model_checkpoints/ConvLSTM_150.pth
```

<p align="center">
    <img src="assets/penalty.gif" width="400"\>
</p>

### Results

The model reaches a classification accuracy of **91.27%** accuracy on a randomly sampled test set, composed of 20% of the total amount of video sequences from UCF-101. Will re-train this model on the offical train / test splits and post results as soon as I have time.