  Video object segmentation approaches primarily rely on large-scale
pixel-accurate human-annotated datasets for model development. In Dense Video
Object Segmentation (DVOS) scenarios, each video frame encompasses hundreds of
small, dense, and partially occluded objects. Accordingly, the labor-intensive
manual annotation of even a single frame often takes hours, which hinders the
development of DVOS for many applications. Furthermore, in videos with dense
patterns, following a large number of objects that move in different directions
poses additional challenges. To address these challenges, we proposed a
semi-self-supervised spatiotemporal approach for DVOS utilizing a
diffusion-based method through multi-task learning. Emulating real videos'
optical flow and simulating their motion, we developed a methodology to
synthesize computationally annotated videos that can be used for training DVOS
models; The model performance was further improved by utilizing weakly labeled
(computationally generated but imprecise) data. To demonstrate the utility and
efficacy of the proposed approach, we developed DVOS models for wheat head
segmentation of handheld and drone-captured videos, capturing wheat crops in
fields of different locations across various growth stages, spanning from
heading to maturity. Despite using only a few manually annotated video frames,
the proposed approach yielded high-performing models, achieving a Dice score of
0.82 when tested on a drone-captured external test set. While we showed the
efficacy of the proposed approach for wheat head segmentation, its application
can be extended to other crops or DVOS in other domains, such as crowd analysis
or microscopic image analysis.
