Multi‐frame based adversarial learning approach for video surveillance
作者：
Highlights：
• Recent advancements in transportation systems, artificial intelligence and surveillance cameras have shown that the extracting moving or foreground objects in video i.e. Foreground-background Segmentation (FBS), plays an important role in many automated video processing applications.
• The temporal encoding mechanism-based generative adversarial learning framework for the foreground-background segmentation task is proposed with multi-scale inception and residual connection based dense module.
• Unlike traditional training-testing of the network, we have analysed the learning of network in different ways like cross-data, disjoint and global training-testing for FBS. To the best of our knowledge, this is the first end-to-end recurrent generative adversarial learning framework with multi-scale inception and residual connection based dense module for FBS.
• The effectiveness (qualitatively and quantitatively) of the proposed approach is examined with different training-testing techniques (cross-data, disjoint and global training-testing) on three different benchmark video databases, namely Grayscale-Thermal Foreground Detection (GTFD) [6], Densely Annotated VIdeo Segmentation (DAVIS)-2016, ChangeDetection.net (CDnet)-2014) for FBS task.
摘要
•Recent advancements in transportation systems, artificial intelligence and surveillance cameras have shown that the extracting moving or foreground objects in video i.e. Foreground-background Segmentation (FBS), plays an important role in many automated video processing applications.•The temporal encoding mechanism-based generative adversarial learning framework for the foreground-background segmentation task is proposed with multi-scale inception and residual connection based dense module.•Unlike traditional training-testing of the network, we have analysed the learning of network in different ways like cross-data, disjoint and global training-testing for FBS. To the best of our knowledge, this is the first end-to-end recurrent generative adversarial learning framework with multi-scale inception and residual connection based dense module for FBS.•The effectiveness (qualitatively and quantitatively) of the proposed approach is examined with different training-testing techniques (cross-data, disjoint and global training-testing) on three different benchmark video databases, namely Grayscale-Thermal Foreground Detection (GTFD) [6], Densely Annotated VIdeo Segmentation (DAVIS)-2016, ChangeDetection.net (CDnet)-2014) for FBS task.
论文关键词：Temporal sampling,Multi-scale adversarial learning,Foreground-background segmentation and video surveillance
论文评审过程：Received 12 October 2020, Revised 13 September 2021, Accepted 23 September 2021, Available online 25 September 2021, Version of Record 12 October 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108350
