Segmentation of Motion Objects in Video Frames using Deep Learning

The segmentation of the moving objects in the video sequences is one of the most usable series in the machine vision field, which has absorbed the consideration of researchers in the latter decades. It is a challenging task, especially when there are several motion objects in the video, and then the system needs to discover the objects that should be segmented among the trail. Therefore, in this article, we present a new method to segment several motion objects at the same time. In this work, the propagation of the credence of the confidently-estimated frames by fine-tuning the DCNN model with the other frames is the main idea. We exert a DCNN model (which is pre-trained) for the frames to estimate the class of the object; then, we gather the frames where the approximation is locally or globally reliable. In the following, we apply a collection of the frames of CE as the training set to fine-tune the pre-trained network with the existing examples in a video. Our proposed model provides acceptable results, which are better than the results of similar models. These comparisons are made in the dataset of YouTube-VOS. Also, our presented approach is applied in the dataset of DAVIS-2017 and the obtained results are better than the results of the similar works.

@article{JIANG2023,
  title     = {Segmentation of Motion Objects in Video Frames using Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Feng JIANG and Jiao LIU and Jiya TIAN},
  doi       = {10.14569/IJACSA.2023.0140902},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140902}
}