README.md

---
license: mit
task_categories:
- question-answering
language:
- en
pretty_name: TLV Dataset
---
# Temporal Logic Video (TLV) Dataset
<!-- PROJECT LOGO -->
<br />
<div align="center">
  <h3 align="center">Temporal Logic Video (TLV) Dataset</h3>
  <p align="center">
    Synthetic and real video dataset with temporal logic annotation
    <br />
    <a href="https://github.com/UTAustin-SwarmLab/temporal-logic-video-dataset"><strong>Explore the GitHub »</strong></a>
    <br />
    <br />
    <a href="https://anoymousu1.github.io/nsvs-anonymous.github.io/">NSVS-TL Project Webpage</a>
    ·
    <a href="https://github.com/UTAustin-SwarmLab/Neuro-Symbolic-Video-Search-Temploral-Logic">NSVS-TL Source Code</a>
  </p>
</div>

## Overview

The Temporal Logic Video (TLV) Dataset addresses the scarcity of state-of-the-art video datasets for long-horizon, temporally extended activity and object detection. It comprises two main components:

1. Synthetic datasets: Generated by concatenating static images from established computer vision datasets (COCO and ImageNet), allowing for the introduction of a wide range of Temporal Logic (TL) specifications.
2. Real-world datasets: Based on open-source autonomous vehicle (AV) driving datasets, specifically NuScenes and Waymo.

## Table of Contents

- [Dataset Composition](#dataset-composition)
- [Dataset](#dataset)
- [License](#license)


## Dataset Composition

### Synthetic Datasets
- Source: COCO and ImageNet
- Purpose: Introduce artificial Temporal Logic specifications
- Generation Method: Image stitching from static datasets

### Real-world Datasets
- Sources: NuScenes and Waymo
- Purpose: Provide real-world autonomous vehicle scenarios
- Annotation: Temporal Logic specifications added to existing data

## Dataset 
Though we provide a source code to generate datasets from different data sources, we release a dataset v1 as a proof of concept. 

### Dataset Structure

We provide a v1 dataset as a proof of concept. The data is offered as serialized objects, each containing a set of frames with annotations.

#### File Naming Convention
`\<tlv_data_type\>:source:\<datasource\>-number_of_frames:\<number_of_frames\>-\<uuid\>.pkl`

#### Object Attributes
Each serialized object contains the following attributes:
- `ground_truth`: Boolean indicating whether the dataset contains ground truth labels
- `ltl_formula`: Temporal logic formula applied to the dataset
- `proposition`: A set of propositions for ltl_formula
- `number_of_frame`: Total number of frames in the dataset
- `frames_of_interest`: Frames of interest which satisfy the ltl_formula
- `labels_of_frames`: Labels for each frame
- `images_of_frames`: Image data for each frame

You can download a dataset from here. The structure of the dataset is as follows: serializer.
   ```
   tlv-dataset-v1/
   ├── tlv_real_dataset/
   ├──── prop1Uprop2/
   ├──── (prop1&prop2)Uprop3/
   ├── tlv_synthetic_dataset/
   ├──── Fprop1/
   ├──── Gprop1/
   ├──── prop1&prop2/
   ├──── prop1Uprop2/
   └──── (prop1&prop2)Uprop3/
   ```
#### Dataset Statistics
1. Total Number of Frames

| Ground Truth TL Specifications | Synthetic TLV Dataset | | Real TLV Dataset | |
| --- | ---: | ---: | ---: | ---: |
| | COCO | ImageNet | Waymo | Nuscenes |
| Eventually Event A | - | 15,750 | - | - |
| Always Event A | - | 15,750 | - | - |
| Event A And Event B | 31,500 | - | - | - |
| Event A Until Event B | 15,750 | 15,750 | 8,736 | 19,808 |
| (Event A And Event B) Until Event C | 5,789 | - | 7,459 | 7,459 |

2. Total Number of datasets

| Ground Truth TL Specifications | Synthetic TLV Dataset | | Real TLV Dataset | |
| --- | ---: | ---: | ---: | ---: |
| | COCO | ImageNet | Waymo | Nuscenes |
| Eventually Event A | - | 60 | - | - |
| Always Event A | - | 60 | - | - |
| Event A And Event B | 120 | - | - | - |
| Event A Until Event B | 60| 60 | 45| 494 |
| (Event A And Event B) Until Event C | 97 | - | 30 | 186|

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Connect with Me
<p align="center">
 <em>Feel free to connect with me through these professional channels:</em>
</p>
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## Citation
If you find this repo useful, please cite our paper:
```bibtex
@inproceedings{Choi_2024_ECCV,
  author={Choi, Minkyu and Goel, Harsh and Omama, Mohammad and Yang, Yunhao and Shah, Sahil and Chinchali, Sandeep},
  title={Towards Neuro-Symbolic Video Understanding},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  month={September},
  year={2024}
}
```