Feature-based visual tracking for agricultural implements

Systems which utilize implement-mounted cameras for machinery feedback, such as row crop cultivators and sectional sprayers, can be upgraded to provide high-accuracy ground speed and tracking data using visual tracking algorithms. Vector data produced by visual tracking can be incorporated into control systems to compensate for implement dynamics in complement with RTK-GNSS receivers and other sensors. Variations of the SURF, SIFT, and ORB feature-descriptor algorithms were evaluated using a dataset of 640×480 pixel videos on six surfaces (gravel, asphalt, grass, seedlings, residue, and pasture) for speeds from 1 to 5 m/s. Feature-descriptor matching of consecutive video frames was tested using two methods of the k-Nearest Neighbors (kNN) algorithm: (1) 1NN with cross-checking, and (2) 2NN with the ratio-test. Ground speed and tracking direction were calculated using a fast histogram filter to reject outliers between frames. Compared to RTK-GNSS, ORB with CLAHE pre-processing (CLORB) and 1NN cross-checking was found to be the most robust with respect to real-time applications. For 95% of measurements, CLORB achieved an error of 0.23 m/s. Similar accuracy was achieved with SURF, U-SURF, and SIFT, but CLORB was capable of producing vector data in real-time (approximately 25 Hz), whereas SURF, U-SURF, and SIFT were only capable of 15 Hz or less.