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We present a novel fruit counting pipeline that combines deep segmentation,frame to frame tracking, and 3D localization to accurately count visible fruitsacross a sequence of images. Our pipeline works on image streams from amonocular camera, both in natural light, as well as with controlledillumination at night. We first train a Fully Convolutional Network (FCN) andsegment video frame images into fruit and non-fruit pixels. We then trackfruits across frames using the Hungarian Algorithm where the objective cost isdetermined from a Kalman Filter corrected Kanade-Lucas-Tomasi (KLT) Tracker. Inorder to correct the estimated count from tracking process, we combine trackingresults with a Structure from Motion (SfM) algorithm to calculate relative 3Dlocations and size estimates to reject outliers and double counted fruittracks. We evaluate our algorithm by comparing with ground-truthhuman-annotated visual counts. Our results demonstrate that our pipeline isable to accurately and reliably count fruits across image sequences, and thecorrection step can significantly improve the counting accuracy and robustness.Although discussed in the context of fruit counting, our work can extend todetection, tracking, and counting of a variety of other stationary features ofinterest such as leaf-spots, wilt, and blossom.

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