Object detection in computer vision involves identifying and localizing objects within images or video frames. It uses techniques like feature extraction, object localization, and classification. Modern methods, often based on deep learning, achieve high accuracy and efficiency. Applications include autonomous vehicles, surveillance, retail, healthcare, and human-computer interaction.

Scene understanding in computer vision involves analyzing visual data to interpret objects and their relationships in a scene. It includes tasks like object detection and segmentation, often utilizing advanced techniques like deep learning for accuracy. Applications span autonomous vehicles, augmented reality, and surveillance.

Face detection and recognition in computer vision involve identifying and distinguishing faces, using algorithms to detect facial features and match them against known identities, with deep learning enhancing accuracy. Applications span security systems, biometric authentication, social media tagging, and personalized user experiences.

Motion analysis in computer vision tracks and understands object movement using algorithms, detecting, tracking, and analyzing motion patterns over time. Techniques like optical flow enable precise motion estimation, serving applications in action recognition, surveillance, sports analysis, and human-computer interaction.

Text recognition (OCR) in computer vision extracts and interprets text from images or documents using algorithms, enabling conversion into editable and searchable text. Advanced techniques like deep learning enhance accuracy, serving applications in document digitization, translation, and accessibility.