Datasets:
data_type
stringclasses 2
values | source
stringclasses 4
values | tl_category
stringclasses 5
values | file_count
int64 30
494
| frame_count
int64 5.79k
31.5k
|
---|---|---|---|---|
tlv_real_dataset | nuscenes | (prop1&prop2)Uprop3 | 186 | 7,459 |
tlv_real_dataset | waymo | (prop1&prop2)Uprop3 | 30 | 5,789 |
tlv_real_dataset | nuscenes | prop1Uprop2 | 494 | 19,808 |
tlv_real_dataset | waymo | prop1Uprop2 | 45 | 8,736 |
tlv_synthetic_dataset | coco | (prop1&prop2)Uprop3 | 97 | 28,900 |
tlv_synthetic_dataset | imagenet | Fprop1 | 60 | 15,750 |
tlv_synthetic_dataset | imagenet | Gprop1 | 60 | 15,750 |
tlv_synthetic_dataset | coco | prop1&prop2 | 120 | 31,500 |
tlv_synthetic_dataset | coco | prop1Uprop2 | 60 | 15,750 |
tlv_synthetic_dataset | imagenet | prop1Uprop2 | 60 | 15,750 |
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:
- 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.
- 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 labelsltl_formula
: Temporal logic formula applied to the datasetproposition
: A set of propositions for ltl_formulanumber_of_frame
: Total number of frames in the datasetframes_of_interest
: Frames of interest which satisfy the ltl_formulalabels_of_frames
: Labels for each frameimages_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
- 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 |
- 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}
}
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