---
license: cc-by-4.0
pretty_name: Overhead Traffic Anomalies (OTA)
language: en
tags: [traffic, computer-vision, anomaly-detection, surveillance, trajectories, ranking, ndcg]
task_categories:
  - video-classification
  - time-series-forecasting
  - object-detection
---

# Overhead Traffic Anomalies (OTA)

This dataset contains traffic anomalies from **static overhead cameras** (intersections/roundabouts). It includes **32 anomaly categories** with a **relevance mapping** for severity-aware evaluation. For each camera, we provide video frames with YOLOv8 detections, including 24 h of training footage and 48 h of test footage with **1027 anomaly annotations**.

*Created as part of the Master’s thesis by Hanna Lichtenberg (2025), in cooperation with Starwit Technologies GmbH. See the thesis for details and baseline NDCG@50 results.*

## What’s inside

- **Three camera scenes** (each with `traindata` and `testdata`).
- **Frames** (320×180) as WebDataset `.tar` shards (`frames-xxxxx.tar`) plus a global `index.csv`.
- **Object detections** (YOLOv8) with tracking IDs and geo-coordinates in `object_detections.json`.
- **Anomaly annotations** in `anomaly-labels.csv` (test split only).
- **Label dictionary** in `event_labels.txt` and **relevance mapping** in `relevance_mapping.json`.

#### Anomaly labels 
- `label ∈ {-1, 0, 1, …, 32}`
  - `-1` = marks detection/tracking artifacts so they don’t bias evaluation (e.g., spurious box, ID switch) 
  - `1…32` = anomaly categories (see `event_labels.txt`)
  - unlabeled trajectories are treated as normal (label `0`)
- **Relevance degrees** (0–4) for severity-aware evaluation in `relevance_mapping.json`. The category→relevance mapping is subjective and may be adapted to match different application priorities.
  - `0`: FP/uninteresting
  - `1`: rather uninteresting anomaly
  - `2`: relevant anomaly
  - `3`: high relevance
  - `4`: critical relevance (dangerous behavior)


#### Example anomaly categories

- **Wrong-way driving** *(IDs: 22, 23)* — vehicle travels against permitted direction
- **Fast driving** *(ID: 11)* — reckless speeding relative to scene context
- **Traffic tie-up** *(ID: 16)* — blockage or standstill due to congestion/obstruction
- **Cutting off another vehicle** *(IDs: 18, 19)* — failing to yield / forcing an agent to brake
- **Broken-down vehicle** *(ID: 25)* — stationary/disabled vehicle on a public road.
- **Getting off the road** *(IDs: 28, 29)* — getting off the roadway / parking on sidewalk


<p align="center">
  <figure style="display:inline-block; text-align:center; margin:0 1px;">
    <img src="examples/wrong_way_driver.png" alt="Wrong-way driving" width="320">
    <figcaption>Wrong-way driving (23)</figcaption>
  </figure>
  <figure style="display:inline-block; text-align:center; margin:0 1px;">
    <img src="examples/almost_collision.png" alt="Cutting off (almost collision)" width="320">
    <figcaption>Cutting off (almost collision) (19)</figcaption>
  </figure>
  <figure style="display:inline-block; text-align:center; margin:0 1px;">
    <img src="examples/parking_on_sidewalk.png" alt="Parking on sidewalk" width="320">
    <figcaption>Parking on sidewalk (29)</figcaption>
  </figure>
  <figure style="display:inline-block; text-align:center; margin:0 1px;">
    <img src="examples/broken_down_car.png" alt="Broken-down vehicle" width="320">
    <figcaption>Broken-down vehicle moved by people (25)</figcaption>
  </figure>
</p>

#### File formats

- **Frames**: WebDataset shards `frames-*.tar` with JPEGs named `frame_<timestamp_ms>_<running_index>.jpg`.

- **index.csv**: maps each frame to its `timestamp` and `shard`, enabling alignment.

- **object_detections.json** (per scene/split): array of per-timestamp records with `timestamp` (UNIX ms), `frame_index`, `frame_key` (JPEG filename), `shard` (tar file), and a `detections` list. Each detection has `class_id` (YOLOv8), `object_id` (stable track ID), `longitude`/`latitude`, `boundingbox` normalized to `[0,1]`, and `confidence`. The `object_id` persists across frames, enabling trajectory-level analyses.

- **anomaly-labels.csv** (test only): CSV with columns `object_id,start_timestamp,end_timestamp,label`. Contains labels for true anomalies (1–32) and input errors (−1).

## Intended use

- **Tasks:** anomaly detection; severity-aware ranking; robustness to detection/tracking noise.
- **Metrics:** NDCG (severity-aware ranking); AU-PR, AU-ROC.

## Quick start

You can align frames, detections, and labels using `index.csv` timestamps and `object_id`s.

```python
import pandas as pd

# Example: load test annotations for a scene
root = "MononElmStreetNB/testdata"
labels = pd.read_csv(f"{root}/anomaly-labels.csv")  # object_id, start_timestamp, end_timestamp, label
index_df = pd.read_csv(f"{root}/index.csv")         # frame_key, timestamp, shard

# Get all frames within an anomaly interval (timestamps are UNIX ms)
def frames_for_interval(ts_start, ts_end, index_df):
    return index_df[(index_df["timestamp_utc_ms"] >= ts_start) & (index_df["timestamp_utc_ms"] <= ts_end)]

rows = frames_for_interval(labels.iloc[0].start_timestamp, labels.iloc[0].end_timestamp, index_df)
print(rows.head())
```
If you prefer streaming frames from `.tar` shards, consider the [webdataset](https://github.com/webdataset/webdataset) library.

## License & Attribution

Data © 2025 Starwit Technologies GmbH & Hanna Lichtenberg - Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

**Please cite:**  
> H. Lichtenberg, *Anomaly Detection in Traffic Applications: A Probabilistic Forecasting Approach Based on Object Tracking*, Master’s thesis, 2025.

**Acknowledgments:**  
Created in cooperation with **Starwit Technologies GmbH**.  
Detections use YOLOv8; geo-mapping via the **Starwit Awareness Engine**.