Cited by 135Smart Agriculture and AIVideo Surveillance and Tracking MethodsAdvanced Neural Network Applications

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Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion

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Liu, Xu, Chen, Steven W., Aditya, Shreyas, Sivakumar, Nivedha, Dcunha, Sandeep, Qu, Chao, Taylor, Camillo J., Das, Jnaneshwar, Kumar, Vijay·2018·2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

DOI 10.1109/iros.2018.8594239

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Abstract

We present a novel fruit counting pipeline that combines deep segmentation, frame to frame tracking, and 3D localization to accurately count visible fruits across a sequence of images. Our pipeline works on image streams from a monocular camera, both in natural light, as well as with controlled illumination at night. We first train a Fully Convolutional Network (FCN) and segment video frame images into fruit and non-fruit pixels. We then track fruits across frames using the Hungarian Algorithm where the objective cost is determined from a Kalman Filter corrected Kanade-Lucas-Tomasi (KLT) Tracker. In order to correct the estimated count from tracking process, we combine tracking results with a Structure from Motion (SfM) algorithm to calculate relative 3D locations and size estimates to reject outliers and double counted fruit tracks. We evaluate our algorithm by comparing with ground-truth human-annotated visual counts. Our results demonstrate that our pipeline is able to accurately and reliably count fruits across image sequences, and the correction step can significantly improve the counting accuracy and robustness. Although discussed in the context of fruit counting, our work can extend to detection, tracking, and counting of a variety of other stationary features of interest such as leaf-spots, wilt, and blossom.

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Authors: Liu, X., Chen, S. W., Aditya, S., Sivakumar, N., Dcunha, S., Qu, C., Taylor, C. J., Das, J., & Kumar, V.
Affiliations: University of Pennsylvania (US)
ORCID: Liu, Xu
Chen, Steven W.
Taylor, Camillo J.
Das, Jnaneshwar
Kumar, Vijay
Venue: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher: IEEE
Published: 2018-10
Pages: 1045-1052
ISSN: 2153-0858 (ISSN-L)
DOI: 10.1109/iros.2018.8594239
OpenAlex: W2963353662
Cited by: 135
References: 21
Citations by year: 2026: 7 · 2025: 13 · 2024: 18 · 2023: 23 · 2022: 18 · 2021: 22 · 2020: 21 · 2019: 13
FWCI: 40.72
Type: conference paper
Language: en
Open access: green
arxiv.org
Locations: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: U.S. Department of Agriculture (2015-67021-23857)
University of Pennsylvania
Washington State University
National Institute of Food and Agriculture (67021)
Topics: Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: fruit counting, fruit segmentation, object tracking, structure from motion, 3D localization, monocular camera, nighttime imaging, fully convolutional network, Kalman filter, Kanade-Lucas-Tomasi tracker, Hungarian algorithm, outlier removal, double-counting correction, plant phenotyping

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Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion

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