Dataset Card for RetailAction

This is a FiftyOne dataset with 21000 samples.

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/RetailAction")

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

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Dataset Details

Dataset Description

RetailAction is designed for spatio-temporal localization of customer–product interactions (take, put, touch) across synchronized multi-view ceiling-mounted cameras in real stores.

• Curated by: Standard AI — Davide Mazzini, Alberto Raimondi, Bruno Abbate, Daniel Fischetti, David M. Woollard
• License: Standard AI proprietary license (see LICENSE)
• Paper: RetailAction: Dataset for Multi-View Spatio-Temporal Localization of Human-Object Interactions in Retail — ICCV 2025 Retail Vision Workshop

Dataset Sources

• Repository: standard-cognition/RetailAction on Hugging Face
• Paper: ICCV 2025 RetailAction paper

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FiftyOne Dataset Structure

The dataset is a grouped video dataset (media_type = "group"). Each RetailAction sample folder maps to one FiftyOne Group with two video slices: rank0 (default) and rank1. Each slice is a fo.Sample pointing to the respective .mp4 file, with all annotations stored per slice using the camera-specific coordinates.

Top-level dataset properties

dataset.media_type         # "group"
dataset.group_field        # "group"
dataset.group_slices       # ["rank0", "rank1"]
dataset.group_media_types  # {"rank0": "video", "rank1": "video"}
dataset.default_group_slice  # "rank0"
dataset.skeletons          # {"pose_keypoints": <KeypointSkeleton>}

Sample-level fields

These fields are present on every fo.Sample in both rank0 and rank1 slices.

Field	Type	Description
sample_id	str	Zero-padded folder name, e.g. "000000"
split	str	"train", "validation", or "test"
tags	list[str]	Also contains the split name for tag-based filtering
segment_duration	float	Original video segment duration in seconds (0.9–37s)
has_frame_timestamps	bool	False for ~12% of samples where frame timestamps were not recorded
actions	fo.TemporalDetections	One fo.TemporalDetection per annotated action
interaction_points	fo.Detections	One fo.Detection per annotated interaction point

actions — fo.TemporalDetections

Each fo.TemporalDetection in sample.actions.detections represents one human–object interaction:

Attribute	Type	Description
label	str	Action class: "take", "put", or "touch"
support	[int, int]	[first_frame, last_frame] (1-based, inclusive) derived from normalized [start, end] × total frames

interaction_points — fo.Detections

Each fo.Detection in sample.interaction_points.detections marks where the hand contacts the shelf item. Detections are parallel in order to sample.actions.detections (index i in both refers to the same action).

Attribute	Type	Description
label	str	Action class: "take", "put", or "touch"
bounding_box	[x, y, w, h]	Small 4%×4% box centered on the interaction point (normalized, for App visibility)
interaction_x	float	Raw normalized x-coordinate of the interaction point (for metric computation)
interaction_y	float	Raw normalized y-coordinate of the interaction point (for metric computation)

Note: interaction_x / interaction_y preserve the exact annotated point coordinates for use in the paper's m_px_factor-based spatial distance metric. The bounding box is a visualization convenience only.

Frame-level fields

These fields are stored in sample.frames[i] (1-indexed) and are populated for each of the up to 32 selected video frames. Frames with no detected pose have pose_keypoints = None.

Field	Type	Description
pose_keypoints	fo.Keypoints	Body pose for the subject of interest
face_position	fo.Keypoint	Head center point of the subject of interest
sampling_score	float	Motion-aware frame importance score (higher = more hand movement)

pose_keypoints — fo.Keypoints

Each frame's fo.Keypoints contains one fo.Keypoint with label "person" representing the full-body pose of the interaction subject.

• Points: Fixed-length list of 24 [x, y] coordinates in normalized frame space, one per joint in JOINT_ORDER. Missing joints (not detected by the pose model for that frame/view) are [nan, nan].
• Confidence: Parallel list of 24 raw heatmap activation scores from the PersonLab model. Missing joints have nan. Scores are not probabilities — they are uncalibrated logit-like values typically in [0.06, 1.41]; values >1.0 are possible.

The canonical 24-joint ordering (JOINT_ORDER):

 0: top_of_head      1: nose             2: neck
 3: left_ear         4: right_ear        5: left_eye         6: right_eye
 7: left_shoulder    8: right_shoulder
 9: left_elbow      10: right_elbow
11: left_wrist      12: right_wrist
13: left_hand       14: right_hand
15: middle_of_waist
16: left_hip        17: right_hip
18: left_knee       19: right_knee
20: left_ankle      21: right_ankle
22: left_foot       23: right_foot

The dataset's skeletons["pose_keypoints"] stores the fo.KeypointSkeleton with this ordering and 25 bone connectivity edges, enabling automatic skeleton rendering in the FiftyOne App.

Important notes on pose data:

• Joint sets vary per frame and per camera view — one view may detect the left arm while the other detects the lower body.
• Coordinates are quantized to a 1/64 grid (PersonLab's heatmap resolution), giving a step size of 0.015625.
• ~9% of samples have at least one null pose entry due to the tracker losing the subject mid-segment.
• Only the subject of interest (the person performing the labeled interaction) has pose data. Other people in frame have no annotations.

face_position — fo.Keypoint

Single-point keypoint with label "face" marking the detected head center of the subject of interest.

• Points: [[x, y]] — normalized continuous float coordinates from the face detector (not quantized, unlike pose).
• Present for all frames where the subject's face was detected.

sampling_score — float

Motion-aware importance score computed from the velocity and acceleration of the subject's hands. Higher scores indicate frames with significant hand movement. Used during dataset construction to select the most informative ≤32 frames from longer original segments.

• For ~12% of samples (has_frame_timestamps = False), scores are matched positionally (score i → frame i) rather than by timestamp.
• For the remainder, scores are matched to the nearest timestamp from the original dense score timeline.

Querying examples

import fiftyone as fo

ds = fo.load_dataset("RetailAction")

# Filter to samples with at least one action
ds_with_actions = ds.filter_labels("actions", fo.ViewField("support").length() > 0)

# Get only test samples
test_view = ds.match_tags("test")

# Get multi-action samples (2+ actions in one segment)
multi_action = ds.select_group_slices("rank0").filter_labels(
    "actions",
    fo.ViewField("detections").length() >= 2,
    only_matches=False
).match(fo.ViewField("actions.detections").length() >= 2)

# Filter to frames where pose was detected
frames_with_pose = ds.match_frames(fo.ViewField("pose_keypoints") != None)

# Switch to the second camera view
ds.group_slice = "rank1"

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Dataset Creation

Curation Rationale

RetailAction was created to fill a gap in existing action recognition datasets: no large-scale dataset provided spatio-temporal localization of interactions in real retail stores from multiple synchronized camera views. Prior retail datasets (MERL Shopping, RetailVision) were small, lab-based, or single-view. General-purpose datasets (Kinetics, AVA) lacked retail context and provided bounding boxes around people rather than precise interaction points.

Data Collection and Processing

Data was collected over multiple years from 10 operational US convenience stores. An automated pipeline handled the full flow from raw continuous camera streams to annotated clips:

1. 360-degree cameras (2880×2880, 30 FPS) mounted at ~2.5m ceiling height provided continuous multi-TB/day streams.
2. A custom PersonLab model fine-tuned on 360-degree top-view footage estimated 2D poses per person per frame.
3. Multi-view 3D pose reconstruction triangulated per-camera tracklets into unified 3D tracks.
4. A kinematic GCN (based on ST-GCN) operating on 3D poses and shelf geometry detected candidate interaction intervals, filtering out walking and browsing.
5. A camera scoring algorithm selected the two best views per interaction based on occlusion, body visibility, and hand joint visibility.
6. Motion-aware frame subsampling down-sampled each clip to ≤32 frames by prioritizing frames with high hand velocity/acceleration.
7. Anonymization applied facial blurring and timestamp scrubbing (all timestamps are relative to 1970-01-01T00:00:00).

Annotations

Annotations were produced through a two-step human annotation process:

Step 1 — Binary classification + quality labels: Annotators labeled each segment as interaction/non-interaction and flagged quality issues (bad camera selection, low resolution, too few frames, pose errors). A model-in-the-loop strategy was used: after an initial labeling pass, a model was trained on half the data and the 10% most-disagreed samples were re-reviewed. This cycle repeated three times.

Step 2 — Spatio-temporal fine-grained labels: Annotators marked the precise temporal boundaries of each interaction and spatially localized the exact pixel where the hand contacts the shelf item, for both camera views. In multi-person scenes, a red dot overlay identified the subject of interest to avoid ambiguity.

Action categories:

• take — subject picks up an item from a shelf, fridge, or counter
• put — subject places an item back onto a shelf, fridge, or counter
• touch — hand contact without taking or placing

Labels apply only to interactions with retail shelves — not to shopping baskets, checkout interactions, or other in-store objects.

Post-annotation curation removed single-view segments, low-quality samples, outlier-duration segments, and excess no-interaction segments (capped at 10% of total).

Personal and Sensitive Information

All shoppers consented to recording via terms of service with the collecting organization. Videos have been anonymized:

• Faces are blurred using automated facial detection
• All timestamps are replaced with epoch-relative offsets (starting at 1970-01-01T00:00:00)
• Store names and identifiers are removed or blurred
• Shopper identity labels are withheld — splits are partitioned by shopper but identifiers are not released

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Bias, Risks, and Limitations

Class imbalance: 97.2% of labeled actions are take. The put and touch classes are heavily underrepresented, reflecting real customer behavior rather than a collection artifact.

Store distribution skew: Store 1 accounts for 36.2% of samples; stores 5–10 together account for <10%. Models trained on this dataset may generalize poorly to stores with unusual layouts or lighting.

Top-down perspective: All footage is from ceiling-mounted cameras. Models trained here are not expected to generalize to handheld, egocentric, or eye-level viewpoints.

Partial pose observations: Due to occlusion and the 360-degree fisheye distortion, ~20% of joint detections have low confidence (<0.5), and the detected joint set varies considerably per frame.

Non-uniform frame rate: Clips contain ≤32 frames but span segments of 0.9–37 seconds. The effective frame rate is non-uniform and lower than the original 30 FPS. Temporal models must account for variable time gaps between frames.

Null frame timestamps: ~12% of samples lack frame_timestamps, preventing precise temporal alignment of pose and face data to wall-clock time.

Recommendations

• Apply a confidence threshold (e.g., >0.5) to pose_keypoints.confidence values before using joints for bone-length normalization or feature extraction.
• Use has_frame_timestamps to identify samples where frame-level temporal alignment is unavailable.
• For the spatial localization metric, use interaction_x / interaction_y attributes (not the bounding box center) and apply the per-video m_px_factor computed from bone lengths as described in the paper.
• When evaluating across action classes, report per-class metrics given the severe take/put/touch imbalance.

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Citation

BibTeX:

@inproceedings{mazzini2025retailaction,
  title={RetailAction: Dataset for Multi-View Spatio-Temporal Localization of Human-Object Interactions in Retail},
  author={Mazzini, Davide and Raimondi, Alberto and Abbate, Bruno and Fischetti, Daniel and Woollard, David M.},
  booktitle={ICCV Retail Vision Workshop},
  year={2025}
}

APA:

Mazzini, D., Raimondi, A., Abbate, B., Fischetti, D., & Woollard, D. M. (2025). RetailAction: Dataset for Multi-View Spatio-Temporal Localization of Human-Object Interactions in Retail. ICCV Retail Vision Workshop.