Computer Vision-based Detection and Tracking in the Olive Sorting Pipeline

George Georgiou, Petros Karvelis and Christos Gogos ()
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George Georgiou: University of Ioannina, Department of Informatics and Telecommunications
Petros Karvelis: University of Ioannina, Department of Informatics and Telecommunications
Christos Gogos: University of Ioannina, Department of Informatics and Telecommunications

A chapter in Information and Communication Technologies for Agriculture—Theme II: Data, 2022, pp 131-160 from Springer

Abstract: Abstract A procedure that is currently mostly handled by manual labor is olive fruit sorting by ripeness, as indicated by the color of each individual olive. Small and medium olive tree owners in olive-producing countries have to employee a significant number of workers for several days to perform this tedious task. Big industrial machines, capable of sorting do exist, but their cost is prohibitively high. These machines consist of two main components (a) the computer vision algorithm that is responsible for the detection of the olive fruit that are moving on a conveyor belt in lines and (b) a mechanical part that is responsible to sort the olives. However, advancements in computer vision allow for the implementation of low-cost solutions for the detection of olives. In this work, we present an automated solution that, by using computer vision, can perform the task of detecting the moving olive fruit effectively. Centroids of moving olive fruit are extracted through the Watershed Transform and by employing an Unscented Kalman filter, their position is estimated and tracked in consecutive video frames. Simulation experiments are designed, and the method is tested in various conditions to study its performance. Results suggest that the proposed approach tracks individual olives in synthetic videos created by scrolling images of olives with high accuracy. Even in the presence of induced noise, that resembles motion traces in images, the procedure remains capable of detecting and tracking olive fruit that are not trivially detected by the human eye.

Keywords: Computer vision; Kuhn-Munkres algorithm; Machine learning; Object detection and tracking; Olive sorting; Unscented Kalman Filter; Watershed transform (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-030-84148-5_6

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