We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from the background on the basis of the pixel-level similarity between two object units. The proposed network represents a target object using features from different depth layers in order to take advantage of both the spatial details and the category-level semantic information. Furthermore, we propose a feature compression technique that drastically reduces the memory requirements while maintaining the capability of feature representation. Two-stage training (pretraining and fine-tuning) allows our network to handle any target object regardless of its category (even if the object's type does not belong to the pre-training data) or of variations in its appearance through a video sequence. Experiments on large datasets demonstrate the effectiveness of our model -against related methods -in terms of accuracy, speed, and stability. Finally, we introduce the transferability of our network to different domains, such as the infrared data domain. Recently, Convolutional Neural Networks (CNN) have been extensively applied to various vision tasks given their ability to encapsulate semantic information in the object representation task itself. In spite of this advantage, however, only a few attempts [4, 20, 10] have been made to solve video object segmentation problems using a CNN.
Yoon et al. REF distinguish the foreground objects based on the pixel-level similarity between candidates and templates, which is measured by a matching deep network.
Pixel-Level Matching for Video Object Segmentation Using Convolutional Neural Networks
2017 IEEE International Conference on Computer Vision (ICCV)
2017 IEEE International Conference on Computer Vision (ICCV)
Computer Science