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| license: cc-by-nc-4.0 |
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| # Synthetic ATM Robbery Detection: Multi-Threat Bounding Boxes (CCTV) |
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| ## Overview |
| This is an open-source synthetic dataset specifically engineered to train computer vision models in identifying critical retail security threats, active armed robberies, and victim-offender interactions at automated teller machines (ATMs). |
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| Standard security models often struggle to differentiate between a friendly interaction and a close-proximity physical threat. This dataset addresses that gap by providing realistic, high-angle surveillance frames depicting multi-threat weapons (Handguns and Knives) along with pre-labeled roles for the individuals involved (Offenders vs. Victims). |
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| 🚀 **Need a comprehensive enterprise deployment?** This is a verified sample dataset curated by **Simuletic**. We engineer high-fidelity synthetic datasets to solve edge cases, severe occlusions, and rare security vulnerabilities in AI training. For customized camera angles, lighting distributions, or expanded multi-person scenarios, visit **[simuletic.com/datasets](https://simuletic.com/datasets)**. |
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| ## Key Features |
| * **Multi-Threat Distribution:** Features a balanced variety of handgun and knife robbery scenarios under distinct lighting parameters. |
| * **Proximity Role Assignment:** Programmatically verified spatial annotations that accurately distinguish the active threat agent (Offender) from the targeted civilian (Victim). |
| * **CCTV Degradation Realism:** Implements low-resolution surveillance lens aesthetics, digital noise, compressed JPEG artifacts, and desaturated nocturnal lighting profiles to mimic real-world security hardware. |
| * **100% Privacy-Compliant:** Generated entirely using synthetic pipelines. Contains zero real human likenesses or sensitive biometrics, entirely bypassing GDPR, compliance friction, and privacy risks. |
| * **YOLO Optimized:** Fully structured and pre-annotated in standard normalized YOLO bounding box (`.txt`) format, ready for immediate training pipelines. |
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| ## Dataset Structure & Classes |
| The dataset repository matches standard object detection conventions: |
| * `images/`: High-fidelity synthetic surveillance image files. |
| * `labels/`: Normalized `.txt` files containing target class indices and bounding box anchors. |
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| ### Class Map |
| * **0:** `offender` (The individual initiating the threat, positioned closest to the weapon) |
| * **1:** `victim` (The individual being confronted or withdrawing cash at the terminal) |
| * **2:** `gun` (Handguns, pistols, or compact firearms held at chest or rib height) |
| * **3:** `knife` (Blades, pocket knives, or kitchen knives brandished during the encounter) |
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| ## YAML Configuration |
| To initialize training with Ultralytics YOLO architectures, your `data.yaml` layout should be configured as follows: |
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| ```yaml |
| path: /path/to/dataset |
| train: images |
| val: images |
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| nc: 4 |
| names: |
| 0: offender |
| 1: victim |
| 2: gun |
| 3: knife |
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| Core Use Cases |
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| Real-Time ATM Anomaly Detection: Power edge AI models inside bank lobbies or exterior kiosks to trigger immediate silent alerts when a weapon is brandished. |
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| Behavioral Threat Analysis: Train multi-class neural networks to recognize hostile spatial proximity, physical escalation, and robbery postures before an assault occurs. |
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| Smart City Surveillance: Enhance city-wide monitoring systems to identify vulnerable street-corner transactions and cash machine environments under low light. |
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| Ethics & License |
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| Synthetic Nature: This data is 100% computer-generated. No real individuals were recorded, staged, monitored, or harmed in the creation of this project. |
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| License: CC BY 4.0. You are free to share, adapt, and build upon this data for academic research or commercial production pipelines, provided appropriate attribution credit is given to Simuletic. |
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| Citation |
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| If you utilize this dataset in your models, safety evaluations, or publications, please cite the framework as follows: |
| Plaintext |
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| @dataset{simuletic_atm_robbery_threat_2026, |
| author = {Simuletic Team}, |
| title = {Simuletic Synthetic ATM Robbery Threat Detection Dataset}, |
| year = {2026}, |
| url = {[https://simuletic.com](https://simuletic.com)} |
| } |
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| Feedback & Collaboration |
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| Looking for custom scene layouts, distinct storefront backgrounds, or specific camera HFOV specifications? Reach out directly via simuletic.com or open a ticket in the Discussion tab here on Kaggle! |
