Deep Learning for Object Detection and Segmentation in Videos: Towards an Integration with Domain Knowledge

Reference

A. Ilioudi, A. Dabiri, B. J. Wolf, and B. De Schutter, "Deep learning for object detection and segmentation in videos: Towards an integration with domain knowledge," IEEE Access, vol. 10, pp. 34562-34576, 2022.

Abstract

Deep learning has enabled the rapid expansion of computer vision tasks from image frames to video segments. This paper focuses on the review of the latest research in the field of computer vision tasks in general and on object localization and identification of their associated pixels in video frames in particular. After performing a systematic analysis of the existing methods, the challenges related to computer vision tasks are presented. In order to address the existing challenges, a hybrid framework is proposed, where deep learning methods are coupled with domain knowledge. An additional feature of this survey is that a review of the currently existing approaches integrating domain knowledge with deep learning techniques is presented. Finally, some conclusions on the implementation of hybrid architectures to perform computer vision tasks are discussed.

Publisher page

• Access full text  

Downloads

• PDF of corresponding technical report (478 KB)

BibTeX

@article{IliDab:22-004,
   author  = {Ilioudi, Athina and Dabiri, Azita and Wolf, Ben J. and De
              Schutter, Bart},
   title   = {Deep Learning for Object Detection and Segmentation in Videos:
              {Towards} an Integration with Domain Knowledge},
   journal = {IEEE Access},
   volume  = {10},
   pages   = {34562--34576},
   year    = {2022}
   }

• Download BibTeX entry