minkyuchoi/Temporal-Logic-Video-Dataset

Temporal Logic Video (TLV) Dataset

Temporal Logic Video (TLV) Dataset

Synthetic and real video dataset with temporal logic annotation
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NSVS-TL Project Webpage · NSVS-TL Source Code

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.

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 file for details.

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Citation

If you find this repo useful, please cite our paper:

@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}
}