| Title | Moving Objects Segmentation in Infrared Scene Videos |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | El Rai, Marwa, Al-Saad, Mina, Darweesh, Muna, Al Mansoori, Saeed, Al Ahmad, Hussain, Mansoor, Wathiq |
| Conference Name | 2021 4th International Conference on Signal Processing and Information Security (ICSPIS) |
| Date Published | nov |
| Keywords | background subtraction, Deep Learning, deep video, Infrared Video Sequences, Metrics, Moving Object Segmentation, Object segmentation, pubcrawl, resilience, Resiliency, Scalability, Signal processing algorithms, Streaming media, Thermal sensors, Training, visualization |
| Abstract | Nowadays, developing an intelligent system for segmenting the moving object from the background is essential task for video surveillance applications. Recently, a deep learning segmentation algorithm composed of encoder CNN, a Feature Pooling Module and a decoder CNN called FgSegNET\_S has been proposed. It is capable to train the model using few training examples. FgSegNET\_S is relying only on the spatial information while it is fundamental to include temporal information to distinguish if an object is moving or not. In this paper, an improved version known as (T\_FgSegNET\_S) is proposed by using the subtracted images from the initial background as input. The proposed approach is trained and evaluated using two publicly available infrared datasets: remote scene infrared videos captured by medium-wave infrared (MWIR) sensors and the Grayscale Thermal Foreground Detection (GTFD) dataset. The performance of network is evaluated using precision, recall, and F-measure metrics. The experiments show improved results, especially when compared to other state-of-the-art methods. |
| DOI | 10.1109/ICSPIS53734.2021.9652436 |
| Citation Key | el_rai_moving_2021 |
Moving Objects Segmentation in Infrared Scene Videos