BROWSER-BASED REALTIME OBJECT DETECTION SYSTEM

Object detection is a fundamental task in the field of computer vision that enables machines to identify, classify, and localize multiple objects within images and video streams. With the rapid growth of artificial intelligence and web technologies, there is an increasing demand for lightweight, platform-independent solutions that can perform real-time detection without relying heavily on server-side infrastructure. This project proposes the design and implementation of a browser-based real-time object detection system using TensorFlow.js, a powerful JavaScript library that allows machine learning models to run directly within web browsers. The proposed system utilizes the COCO-SSD (Common Objects in Context – Single Shot Multibox Detector) model, which is pre-trained on a large-scale dataset and capable of detecting up to 80 different object classes, including humans, vehicles, animals, and everyday items. By leveraging WebGL acceleration and client-side computation, the system achieves efficient real-time performance while maintaining reasonable accuracy. The application accesses the user’s webcam feed, processes each frame in real-time, and overlays bounding boxes along with class labels and confidence scores, thereby providing a seamless and interactive user experience. One of the key advantages of this approach is the elimination of back-end servers and external processing dependencies. This not only reduces latency but also enhances privacy and security, as data remains on the client device. Additionally, the system is highly portable and can run across multiple platforms, including desktops, laptops, and mobile devices, without requiring installation of specialized software. The motivation behind this project is driven by the growing need for accessible AI-powered tools that can function in resource-constrained environments or even offline scenarios. The system has a wide range of real-world applications, including retail analytics (customer behavior tracking), smart surveillance (intrusion detection), educational tools (interactive learning), assistive technologies for visually impaired individuals, and advanced human-computer interaction systems. Furthermore, this project establishes a foundation for future enhancements such as integrating custom-trained models for domain-specific detection, implementing alert and notification mechanisms, improving model accuracy and speed, and incorporating edge computing techniques.