Object recognition works by identifying and classifying objects in images or videos. It involves detecting regions of interest, extracting features, and mapping them to predefined categories using algorithms or AI models.
Convolutional neural networks (CNNs) are commonly used for this task. They analyze visual data hierarchically, identifying edges, textures, and shapes to recognize objects. Pre-trained models like YOLO or Faster R-CNN excel in detecting multiple objects simultaneously.
Applications include autonomous vehicles, surveillance systems, and augmented reality, demonstrating the versatility of object recognition technologies.