Real-time Object Detection System: A Deep Learning and OpenCV Approach

Real-time Object Detection System: A Deep Learning and OpenCV Approach

Swati Verma, Suryansh Singh, Er. Ankita Agarwal

Department of Computer Science and Engineering,

Shri Ramswaroop Memorial College of Engineering and Management

Lucknow, UP, India

Abstract— This project demonstrates a real-time object detector powered by deep learning and the SSD method, as well as OpenCV technologies. It has the potential to transform object-detecting processes. Using the power of SSD, this system can recognize and classify objects in live video streaming using a single neural network. SSD is a well-known object detection method that has made such jobs possible. The model was brilliantly trained on a large number of labeled photos using widely available deep-learning frameworks such as TensorFlow. The trained model is then combined with OpenCV to conduct real-time object identification and tracking. The system is assessed using typical benchmark datasets, and the findings demonstrate the effectiveness of the suggested approach. Real-time object detection is crucial in many applications, including surveillance, robotics, and autonomous driving, where the system might be used.

Keywords— Real-time object detection, deep learning, OpenCV, SSD algorithm, neural network, video streaming, Tensorflow, tracking, benchmark datasets, surveillance, robotics, autonomous driving.