Multi-leaf tracking from fluorescence plant videos

Driven by the plant phenotyping application, this paper proposes a new leaf tracking framework to jointly segment, align and track multiple leaves from fluorescence plant videos. Our framework consists of two steps. First, leaf alignment is applied to one video frame to generate a collection of leaf candidates. Second, we define a set of transformation parameters operated on the leaf candidates in order to optimize the alignment in the subsequent video frame according to an objective function. Gradient descent is employed to solve this optimization problem. Experimental results show that the proposed multi-leaf tracking algorithm is superior to the image-based leaf alignment method in terms of three quantitative metrics.