metadata

annotations_creators: []
language: en
size_categories:
  - n<1K
task_categories:
  - object-detection
task_ids: []
pretty_name: TAMPAR
tags:
  - fiftyone
  - image
  - object-detection
  - segmentation
  - keypoints
dataset_summary: >




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 485
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include 'max_samples', etc

  dataset = load_from_hub("voxel51/TAMPAR")


  # Launch the App

  session = fo.launch_app(dataset)

  ```
license: cc-by-4.0

Dataset Card for TAMPAR

This is a FiftyOne dataset with 485 samples.

The samples here are from the test set.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("voxel51/TAMPAR")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

TAMPAR is a novel real-world dataset of parcels

with >900 annotated real-world images with >2,700 visible parcel side surfaces,
6 different tampering types, and
6 different distortion strengths

This dataset was collected as part of the WACV '24 paper "TAMPAR: Visual Tampering Detection for Parcels Logistics in Postal Supply Chains"

Curated by: Alexander Naumann, Felix Hertlein, Laura Dörr and Kai Furmans
Funded by: FZI Research Center for Information Technology, Karlsruhe, Germany
Shared by: Harpreet Sahota, Hacker-in-Residence at Voxel51
License: CC BY 4.0

Dataset Sources

Repository: https://github.com/a-nau/tampar
Paper: https://arxiv.org/abs/2311.03124
Demo: https://a-nau.github.io/tampar/

Uses

Direct Use

Multisensory setups within logistics facilities and a simple cell phone camera during the last-mile delivery, where only a single RGB image is taken and compared against a reference from an existing database to detect potential appearance changes that indicate tampering.

Dataset Structure

COCO Format Annotations

Citation

@inproceedings{naumannTAMPAR2024,
    author    = {Naumann, Alexander and Hertlein, Felix and D\"orr, Laura and Furmans, Kai},
    title     = {TAMPAR: Visual Tampering Detection for Parcels Logistics in Postal Supply Chains},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
    month     = {January},
    year      = {2024},
    note      = {to appear in}
}