videosensorik

Videosensorik is the practice of using video cameras and processing methods to measure properties of the environment, detect events, or infer the state of a scene. It combines hardware (cameras, optics, illumination) with software (image processing, computer vision, and machine learning) to extract information from video streams. It intersects with computer vision, sensor fusion, and robotics and is used in domains such as automotive, surveillance, and industry.

Core tasks include detection, tracking, and recognition; motion estimation and optical flow; scene understanding and activity recognition; depth estimation and 3D reconstruction from monocular or multi-view video; and camera pose estimation. Calibration of intrinsics and extrinsics is important for accurate measurements in multi-camera setups. Video quality factors such as frame rate, resolution, exposure, and compression affect performance, and processing may happen on-device (edge) or in the cloud.

Applications include autonomous driving and driver assistance, robotics, security and surveillance, sports analytics, augmented reality, and industrial inspection. In medicine, video sensing encompasses video-based diagnostics such as endoscopy and other endoscopic imaging where appropriate.

Challenges include variability in lighting, occlusions, motion blur, privacy concerns, data volume, and real-time requirements. Trends in the field include deep learning for video understanding, sensor fusion with other modalities (e.g., LiDAR, radar), multi-camera networks, real-time edge processing, and privacy-preserving video analytics.