Can I read Cascade-SORT: A robust fruit counting approach using multiple features cascade matching on EtoBox?

Cascade-SORT: A robust fruit counting approach using multiple features cascade matching by Leiying He; Fangdong Wu; Xiaoqiang Du; Guofeng Zhang is a Agricultural and Biological Sciences article available to read on EtoBox.

What is Cascade-SORT: A robust fruit counting approach using multiple features cascade matching about?

Estimation of fruit yield is of great importance to agricultural management and production decision-making. Fruit counting based on computer vision is faced with many challenges, particularly dense occlusion and difficult detection. To address the problems that exist in agricultural scenarios, we propose a fruit counting pipeline based on multiple features matching. Fruit counting is regarded as a multiple object tracking problem based on tracking-by-detection framework. The proposed method combines object detection with deep learning, Kalman filter, and cascade matching, which integrated motion and appearance features for frame-by-frame data association. Using the detection results of YOLO-v3, cascade matching is leveraged to associate detection bounding boxes with tracks. In cascade matching, the appearance features of fruit, Mahalanobis distance, and intersection over union metric were fused to match objects frame-by-frame. Mahalanobis distance is used to screen detection bounding boxes initially. Furthermore, the vector of locally aggregated descriptors image retrieval method is used to calculate the similarity of appearance between the two objects. In the final step of cascade

Who reads Cascade-SORT: A robust fruit counting approach using multiple features cascade matching?

Author

Leiying He; Fangdong Wu; Xiaoqiang Du; Guofeng Zhang

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Agricultural and Biological Sciences (Physical Sciences)