Research Topic

Object-Centric Video Analysis and Processing

About this Research Topic

With the rapid growth of video-based applications and services, the size of video data has become extremely huge, and this has greatly tested the computational limits of today’s computing systems and the infeasibility of human intervention. With the rapid advances in hardware technology, there exists a need for new and innovative techniques that can perform the analysis and processing of videos in a fully automatic or semi-automatic way. Often, the objects that are captured in video scenes provide valuable low-level semantics that can be pieced together to construct meaningful information for use in software and systems. The behavior and interaction between objects in the video also provide us higher-level knowledge about the scene, which can help machines to sense and understand the world around us.

The goal of this Research Topic is to solicit original research papers, bringing together researchers from academia and industry to report recent advances in object-centric video analysis and processing.
Several challenges have arisen in object-centric video analysis and processing. Firstly, the advent of Big Data and Deep Learning has rendered object-centric tasks such as object classification, object detection, and tracking including human-centric tasks like action/activity recognition and localization increasingly vital for numerous real-world applications such as surveillance monitoring, product automation, and autonomous driving. Secondly, semantic data associated with objects (e.g. bounding boxes, trajectories) are becoming an essential data type in many modern-day systems, hence introducing new challenges towards efficient data processing and compression. Thirdly, the rapid growth from static single machine processing to dynamic computing among distributed video processing nodes has ushered in the proliferation of edge/fog computing, federated learning, and visual re-identification across camera networks. To meet all these challenges, there is a need to extend existing approaches or explore new feasible techniques.

Themes of interest in this Research Topic include but are not limited to the following:

• Object-centric analysis from video: Object classification, object detection and tracking, semantic/instance/panoptic segmentation
• Object-centric analysis of behaviors: Action/activity recognition and localization, event detection and retrieval, crowd parsing and estimation
• Object-centric re-identification from videos: Person, animal, group, or any objects
• Object-centric video processing: Video restoration, denoising, enhancement, super-resolution
• Low-resolution and scalable video analysis and processing involving objects
• Object-centric video surveillance: Multi-camera surveillance networks and applications, surveillance scene parsing and analysis, object-centric indexing and retrieval
• Distributed computing for object-centric tasks: edge and fog computing, federated learning
• Object-centric processing from various modalities, not limited to UAVs, satellite imagery, dash cams, autonomous vehicles, wearable cameras


Keywords: Object-centric analysis, Object-centric processing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

With the rapid growth of video-based applications and services, the size of video data has become extremely huge, and this has greatly tested the computational limits of today’s computing systems and the infeasibility of human intervention. With the rapid advances in hardware technology, there exists a need for new and innovative techniques that can perform the analysis and processing of videos in a fully automatic or semi-automatic way. Often, the objects that are captured in video scenes provide valuable low-level semantics that can be pieced together to construct meaningful information for use in software and systems. The behavior and interaction between objects in the video also provide us higher-level knowledge about the scene, which can help machines to sense and understand the world around us.

The goal of this Research Topic is to solicit original research papers, bringing together researchers from academia and industry to report recent advances in object-centric video analysis and processing.
Several challenges have arisen in object-centric video analysis and processing. Firstly, the advent of Big Data and Deep Learning has rendered object-centric tasks such as object classification, object detection, and tracking including human-centric tasks like action/activity recognition and localization increasingly vital for numerous real-world applications such as surveillance monitoring, product automation, and autonomous driving. Secondly, semantic data associated with objects (e.g. bounding boxes, trajectories) are becoming an essential data type in many modern-day systems, hence introducing new challenges towards efficient data processing and compression. Thirdly, the rapid growth from static single machine processing to dynamic computing among distributed video processing nodes has ushered in the proliferation of edge/fog computing, federated learning, and visual re-identification across camera networks. To meet all these challenges, there is a need to extend existing approaches or explore new feasible techniques.

Themes of interest in this Research Topic include but are not limited to the following:

• Object-centric analysis from video: Object classification, object detection and tracking, semantic/instance/panoptic segmentation
• Object-centric analysis of behaviors: Action/activity recognition and localization, event detection and retrieval, crowd parsing and estimation
• Object-centric re-identification from videos: Person, animal, group, or any objects
• Object-centric video processing: Video restoration, denoising, enhancement, super-resolution
• Low-resolution and scalable video analysis and processing involving objects
• Object-centric video surveillance: Multi-camera surveillance networks and applications, surveillance scene parsing and analysis, object-centric indexing and retrieval
• Distributed computing for object-centric tasks: edge and fog computing, federated learning
• Object-centric processing from various modalities, not limited to UAVs, satellite imagery, dash cams, autonomous vehicles, wearable cameras


Keywords: Object-centric analysis, Object-centric processing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

27 November 2021 Abstract
26 January 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

27 November 2021 Abstract
26 January 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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