An open-source computer vision tool that detects, tracks, and counts moving objects from cameras and videos.
OpenDataCam is an open-source computer vision tool that uses machine learning to detect, track, and count moving objects from video feeds or recorded videos. It solves the problem of quantifying real-world movements—like traffic flows or pedestrian activity—by providing an accessible, self-hosted platform for object analysis without requiring extensive AI expertise.
Urban planners, traffic engineers, researchers, and IoT developers who need to analyze object movements from video sources for studies, monitoring, or data collection.
Developers choose OpenDataCam for its ease of setup, self-hosted data ownership, and flexibility—it works on edge devices and cloud, supports custom models, and offers both UI and API interfaces for diverse use cases.
An open source tool to quantify the world
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Detects 50+ common object classes out-of-the-box, such as vehicles and pedestrians, making it suitable for traffic studies and urban planning without immediate customization.
Runs on edge devices like NVIDIA Jetson for field use or in cloud data centers, as highlighted in the installation scripts for platforms like nano, xavier, and desktop.
Users retain full ownership of all collected data, avoiding vendor lock-in and ensuring privacy, which is emphasized in the philosophy section.
Offers an easy-to-use UI for drag-and-drop video analysis and a comprehensive API for custom integrations, as shown in the demo videos and API documentation links.
Requires NVIDIA GPU with CUDA and Docker for optimal performance, adding cost and setup complexity, especially for non-technical users, as noted in the pre-requisites.
While custom model training is supported, it requires manual configuration and expertise, with documentation pointing to external resources for neural network weights.
On devices without GPU acceleration, such as CPUs only, real-time processing may be slow or impractical, limiting deployment flexibility.