This project focuses on building a system that can automatically analyze and detect objects in images using Computer Vision technologies.
By leveraging AI models, the system can understand visual data and perform tasks such as detection, recognition, and estimation in real time.
Computer Vision is a key area of Artificial Intelligence that enables systems to understand and process visual information from images and videos.
With recent advancements in AI, computer vision models can now detect objects, recognize patterns, and extract meaningful insights with high accuracy. These capabilities are wid
In this project, TechTIQ Inc. applies modern computer vision techniques to build a fast and reliable system for real-time object analysis.
Our Approaches
This approach focuses on detecting faces in real time from camera or video streams (WebRTC). To achieve both speed and accuracy, TechTIQ Inc. applied a lightweight deep learning model (BlazeFace) that is optimized for fast inference. This allows the system to deliver high performance across both mobile and web environments while maintaining stable and accurate face detection during live streaming.
This approach enables the system to detect and track hand movements by identifying key points (landmarks) on the hand. By capturing these positions, the system can support gesture recognition and real-time interaction. This makes it suitable for applications such as gesture control, motion tracking, and other interactive use cases.
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