---
title: 'MUVAD: UAV Anomaly and Moving Object Datasets'
url: https://www.emergentmind.com/topics/moving-uav-vad-dataset-muvad
type: topic
---

# MUVAD: UAV Anomaly and Moving Object Datasets

The Moving UAV VAD Dataset (MUVAD) refers to two distinct publicly available datasets constructed for moving camera Video Anomaly Detection (VAD) and moving-object detection in dynamically captured Unmanned Aerial Vehicle (UAV) video. Both datasets are designed to address limitations of static-camera surveillance benchmarks by introducing real-world scenarios characterized by UAV-induced ego-motion, multi-source motion coupling, and complex backgrounds. MUVAD datasets serve as benchmarks for evaluating algorithms concerning anomaly detection and small-object detection in urban and natural environments, representing high-value resources for the development and validation of motion-robust computer vision methods [2601.11254][2103.11460].

## 1. Dataset Definitions and Scope

Two principal datasets known as MUVAD are referenced in current literature:

**(A) MUVAD (Video Anomaly Detection) [2601.11254]:**
A large-scale, dynamic UAV video dataset created to evaluate VAD algorithms under realistic aerial surveillance conditions with continuously moving camera platforms, focusing on urban traffic anomaly detection.

**(B) MUVAD (PESMOD; Moving Object Detection) [2103.11460]:**
A high-resolution UAV video dataset curated for benchmarking moving-object detection methods where scenarios present challenging small-target detection amidst free-form 6-DoF camera motion, varied environments, and no predefined train/test split.

Both datasets share the goal of advancing detection methodologies capable of handling UAV-induced background variability but differ in annotation granularity, event taxonomy, and intended evaluation protocols.

## 2. Data Acquisition Methodologies

### (A) MUVAD (Video Anomaly Detection)

- **Flight Patterns:** Continuous UAV translation, rotation, and object-tracking maneuvers over urban arterials, intersections, hubs, and construction zones.
- **Capture Protocol:** Normal (training) sequences are obtained via routine UAV patrols; test (anomalous) sequences are collected both on-board and via curated YouTube videos, only retaining those with adequate quality, duration, and exclusive UAV capture.
- **Resolution and Frame Rate:** 852 × 480 pixels, 30 fps.
- **Environmental Conditions:** Diverse lighting (day/night) and weather (sunny/foggy) patterns are represented; all scenes feature substantial background dynamics resulting from UAV movement.

### (B) MUVAD (PESMOD; Moving Object Detection)

- **Source Footage:** Eight video sequences, sourced from freely available drone videos on Pexels.
- **Platform:** Consumer-grade UAVs (models and flight altitudes unspecified) with standard RGB sensors.
- **Resolution:** 1920 × 1080 pixels (Full HD).
- **Environments:** Urban roads, trekking trails, snow fields, and rural landscapes under daytime lighting.
- **Motion Dynamics:** Both slow and rapid UAV-induced camera motion; some sequences include zoom, pitch, and roll changes.

## 3. Annotation Schemes and Quality Control

### (A) MUVAD (Video Anomaly Detection)

- **Labeling Protocol:** Frame-level binary labels (normal = 0, anomaly = 1). Anomaly intervals correspond to the visibility of any of 12 predefined anomalous event types.
- **Annotation Modality:** No bounding box or pixel segmentation; only framewise annotation.
- **Quality Assurance:** Each test video sequence is independently labeled by multiple annotators, with disputed frames reviewed by domain experts before finalization.

### (B) MUVAD (PESMOD; Moving Object Detection)

- **Labeling Protocol:** Axis-aligned bounding boxes for single-class "moving object" (including pedestrians and vehicles), with one bounding box per instance.
- **Annotation Method:** Fully manual; specifics on inter-annotator agreement or additional validation procedures are not detailed.
- **Granularity:** No segmentation masks provided; annotation formats likely text or CSV per frame.

## 4. Dataset Statistics and Splits

### (A) MUVAD (Video Anomaly Detection)

| Split     | Videos | Frames   | Anomaly Events | Anomaly Types | Background Type    |
|-----------|--------|----------|----------------|---------------|--------------------|
| Training  | 46     | 126,254  | 0              | 0             | Dynamic            |
| Testing   | 72     | 96,482   | 240            | 12            | Dynamic            |
| **Total** | 118    | 222,736  | 240            | 12            | Dynamic            |

- **Anomaly Density:** Less than 1% of total frames are labeled anomalous.

### (B) MUVAD (PESMOD; Moving Object Detection)

| Sequence Name          | Frames | Moving-Object Boxes |
|-----------------------|--------|--------------------|
| Pexels-Elliot-road    | 664    | 3,416              |
| Pexels-Miksanskiy     | 729    | 189                |
| Pexels-Shuraev-trekking| 400   | 800                |
| Pexels-Welton         | 470    | 1,129              |
| Pexels-Marian         | 622    | 2,791              |
| Pexels-Grisha-snow    | 115    | 1,150              |
| Pexels-Zaborski       | 582    | 3,290              |
| Pexels-Wolfgang       | 525    | 1,069              |
| **Total**             | 4,107  | 13,834             |

- **Evaluation:** No formal train/test split; all methods are benchmarked on all sequences.

## 5. Task Definitions and Evaluation Metrics

### (A) MUVAD (Video Anomaly Detection)

- **Anomaly Categories:** 12 types, including illegal lane change, emergency lane violation, wrong-way driving, construction zone, vehicle breakdown, animal intrusion, vehicle skidding, vehicle collision, fire incident, roadside deviation, traffic congestion, and pedestrian intrusion.
- **Protocol:** One-class training—models are trained exclusively on normal examples and tested on both normal and anomalous frames.
- **Metrics:** Micro-AUC and Macro-AUC are recommended for performance reporting; usage guidelines emphasize adherence to this protocol.

### (B) MUVAD (PESMOD; Moving Object Detection)

- **Detection Criteria:** A detected box \( R_{\rm DET} \) is a true positive (TP) if \( \frac{\mathrm{area}(R_{\rm GT} \cap R_{\rm DET})}{\mathrm{area}(R_{\rm GT})} \geq 0.2 \).
- **Evaluation Metrics:**
  - Precision, recall, F1-score (standard definitions).
  - Overlap ratio \( O_r = \frac{\mathrm{area}(R_{\rm GT} \cap R_{\rm DET})}{\mathrm{area}(R_{\rm GT})} \) for true positives.
- **Baselines:**
  - MCD (Yi et al., 2013): Grid-based KLT/RANSAC homography with Gaussian background model and neighbor mixing.
  - SCBU (Yun et al., 2017): Motion-compensation backbone with scene-conditional background updating.
  - Proposed method: Lightweight, flow-weighted differencing with adaptive thresholds and neighborhood differencing.

**Comparison of methods (average across all 8 sequences):**

| Metric    | MCD    | SCBU   | Proposed |
|-----------|--------|--------|----------|
| \(O_r\)   | 0.7756 | 0.4949 | 0.6755   |
| Precision | 0.5428 | 0.4268 | 0.5452   |
| Recall    | 0.5607 | 0.4416 | 0.5070   |
| \(F_1\)   | 0.3828 | 0.4108 | 0.4302   |

## 6. Position Among Related UAV Datasets

### (A) MUVAD (Video Anomaly Detection)

| Dataset        | Videos     | Frames   | Events | Anomaly Types | Background     |
|----------------|------------|----------|--------|---------------|---------------|
| CHUK Avenue    |16/21       |30,652    |77      |5              | Static        |
| ShanghaiTech   |238/199     |317,398   |158     |11             | Static        |
| Drone-Anomaly  |37/22       |87,488    |26      |10             | 35% Dynamic   |
| UIT-ADrone     |41/51       |206,194   |1,935   |10             | Static        |
| **MUVAD**      |46/72       |222,736   |240     |12             | Dynamic       |

- MUVAD offers a fully dynamic background, comparable in scale to the largest existing datasets, with broader anomaly coverage and more realistic moving-camera scenarios [2601.11254].

### (B) MUVAD (PESMOD; Moving Object Detection)

- **Resolution and Target Scale:** Full HD, with small target size (tens of pixels) compared to datasets like VIVID and CDnet.
- **Camera Motion:** Supports free 6-DoF UAV maneuvers, beyond CDnet's Pan-Tilt-Zoom or VIVID’s largely planar motion.
- **Advantages:** Provides high-resolution, diverse real-world footage for evaluating detection under strong background variability.
- **Limitations:** Only bounding-box annotations, lack of night/IR imagery, no camera or flight metadata [2103.11460].

## 7. Access, Licensing, and Usage Recommendations

### (A) MUVAD (Video Anomaly Detection)

- Repository: https://github.com/uavano/FTDMamba
- Citation: "FTDMamba: Frequency-Assisted Temporal Dilation Mamba for Unmanned Aerial Vehicle Video Anomaly Detection, C.-Z. Liu et al., IEEE Transactions on [Journal], 2025."
- Usage: Follow one-class training protocol (normal-only training), report Micro/Macro-AUC, adhere strictly to ethical usage—exclude privacy-infringing footage [2601.11254].

### (B) MUVAD (PESMOD; Moving Object Detection)

- Shared under open terms as per [2103.11460]; further details available in the associated publication.
- Intended as a motion-robust detection benchmark for academic research, emphasizing object detection amid UAV-induced ego-motion.

---

The MUVAD datasets collectively provide critical benchmarks for advancing UAV-based anomaly and object detection under unconstrained camera motion, enabling rigorous evaluation of algorithms in dynamic, real-world environments [2601.11254][2103.11460].

Source: https://www.emergentmind.com/topics/moving-uav-vad-dataset-muvad