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:

  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
  1. 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.

Connect with Me

Feel free to connect with me through these professional channels:

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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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