Real-Time Object Detection and Tracking Using Deep Learning

Real-time object detection has always been a broad, dynamic, and challenging domain within computer vision. Image localization entails identifying a singular object within an image, while object detection involves the task of locating multiple objects within an image. This process involves recognizing various semantic entities of digital photography and videos. Real-time detection finds extensive applications such as object tracking, video surveillance, pedestrian identification, people counting, autonomous-driving vehicles, facial detection, sports ball tracking, and more. Convolutional Neural Networks (CNNs) are commonly employed in conjunction with OpenCV (Open-Source Computer Vision), a coding function collection engineered for real-time computer vision, to facilitate object detection.