--- title: "Fall Detection Dataset" description: "Contains 10,000 high-resolution videos of staged human fall events recorded across indoor and outdoor settings using static and moving cameras. Designed for fall detection and…" url: "https://unidata.pro/datasets/fall-detection/" date_modified: "2026-04-24T20:12:33+03:00" language: "en-US" --- Contains 10,000 high-resolution videos of staged human fall events recorded across indoor and outdoor settings using static and moving cameras. Designed for fall detection and computer vision research, this camera dataset provides well-structured metadata, realistic fall scenarios, and consistent 1080p video quality for training and evaluating detection systems. ## Dataset Structure ### The Numbers Section **Numbers list:** - **Number:** 10,000 — **Text:** Videos ### Tooltip Section **Tooltip items:** - **Name:** Computer Vision - **Name:** Machine Learning - **Name:** Public Safety - **Name:** Object Detection - **Name:** Action Recognition ### Dataset Info **Table with data:** | Characteristic | Data | | --- | --- | | Description | Videos of staged human falls in various indoor and outdoor environments, captured in controlled environments from static and moving camera perspectives. | | Data types | Video | | Tasks | Public Safety, Computer Vision | | Total number of files | 10,000 | | Labeling | Metadata (fps, resolution, has_audio, environment_type, time_of_day, weather, camera_motion, participants_count, source_type) | **Media Slider:** - **Video on Slider:** **Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1ejFm6DVvuRuvwjUtyA0bba89RIVRsHqf) ### Technical characteristics **Table with data:** | Characteristic | Data | | --- | --- | | Video extensions | MP4, MOV | | Video Resolutions | 1920 x 1080 | | FPS | 30 | **Source and collection methodology:** Source and collection methodology: Data was captured via digital cameras in controlled, simulated environments. ### Dataset Use Cases - слайдер **Industry Cards:** - **Industry:** Public Safety and Smart Buildings — **Title:** Automated Incident Detection in Public Spaces — **Text:** For smart buildings and public infrastructure, the camera dataset enables detection systems that recognize fall events in corridors, stations, and shared facilities. The dataset consists of controlled yet diverse scenarios, allowing algorithms to distinguish falls from normal movements and reduce false alarms in real-time monitoring systems. - **Industry:** Healthcare and Assisted Living — **Title:** Monitoring Falls in Daily Care Settings — **Text:** This fall detection dataset supports healthcare providers developing camera-based detection systems for assisted living and home care. The videos capture realistic fall events across varied environments, helping train models that identify falls during daily activity. It enables reliable evaluation of fall detection computer vision tools without relying on wearable devices. - **Industry:** Computer Vision and AI Research — **Title:** Benchmarking Fall Detection Algorithms — **Text:** Researchers use this dataset for detection research and algorithm benchmarking. With consistent resolution, clear metadata, and multiple camera perspectives, it supports training and evaluating learning models for fall detection computer vision. The dataset helps compare approaches in action recognition, motion analysis, and anomaly detection. - **Industry:** Rehabilitation and Elderly Care Technology — **Title:** Evaluating Recovery and Safety Systems — **Text:** Developers of rehabilitation and elderly care technology apply the dataset to test fall detection systems in controlled scenarios. The recordings allow analysis of body movements before and after fall events, improving detection accuracy and supporting safer environments for vulnerable users through automated camera-based monitoring. ### Fact **FAQs Heading:** FAQs **List of Questions:** - **Question:** Can I request a sample of Fall Detection Dataset before purchase? — **Answer:** Yes, Unidata provides free sample access to the fall detection dataset for evaluation purposes. This allows you to review video quality, metadata structure, and suitability for detection algorithms and machine learning workflows. - **Question:** What file formats are included in the dataset? — **Answer:** All video files are provided in MP4 and MOV formats with Full HD resolution. The consistent formatting supports seamless integration with machine learning pipelines and detection systems. - **Question:** How was the data collected? — **Answer:** Data was captured using digital cameras in controlled, simulated environments to ensure consistency and safety. The dataset consists entirely of original footage recorded specifically for fall detection and computer vision research. - **Question:** How are Unidata datasets licensed? — **Answer:** Unidata follows a dual-licensing model in which free samples are available for testing and validation. Full access to the fall detection dataset is provided exclusively through purchase. - **Question:** Does Fall Detection Dataset comply with GDPR and data privacy regulations? — **Answer:** Yes, the dataset is curated in compliance with GDPR and applicable data protection laws. All recordings are collected through lawful means with ethical data handling practices. - **Question:** How are Unidata datasets stored and managed? — **Answer:** Unidata securely stores all datasets on AWS cloud infrastructure to ensure scalability and reliability. Storage and management processes comply with ISO 27001 and ISO 27701 information security and privacy standards. - **Question:** How long does it take to receive Fall Detection Dataset? — **Answer:** After submitting a request, Unidata reviews the requirements and completes the necessary documentation. Once the agreement is signed and payment is confirmed, delivery typically occurs within 3–10 days. - **Question:** Why are staged fall scenarios valuable for machine learning? — **Answer:** Controlled fall scenarios provide consistent and accurately labeled examples that allow AI models to learn the visual characteristics of different types of falls. This improves the reliability of training data while enabling objective evaluation of detection algorithms. [Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)