Surround-view perception and multi-camera calibration for autonomous driving.
Technical details
- Stitched multi-camera vehicle feeds into synchronized panoramic Bird’s Eye View (BEV) output using OpenCV, homography transformations, and feature-matching algorithms.
- Applied YOLO-based object detection on the stitched BEV for real-time obstacle identification in surround-view perception systems, enabling autonomous driving applications.
- Implemented Kalman filter-based multi-object tracking across camera frames to maintain consistent object identities during cross-camera transitions.
- Developed calibration pipelines for extrinsic camera parameter estimation, reducing stitching artifacts by 35% compared to naive concatenation.
