About this Research Topic
Video surveillance has increasingly become essential in ensuring public safety and security, particularly with the vast volumes of footage generated daily. Traditional monitoring by humans is inefficient due to the sheer amount of data, necessitating advanced video anomaly detection algorithms. These algorithms are designed to flag unusual activities typically unseen by a human eye, such as unauthorized access or suspicious behaviors. Existing methodologies largely revolve around detecting straightforward anomalies involving isolated objects or actions. However, the dynamic and unpredictable nature of real-world environments poses significant challenges, including the need for algorithms that can understand complex interactions under variable conditions like weather and connectivity issues.
This Research Topic aims to foster the development of innovative video anomaly detection techniques that address multifaceted real-world surveillance challenges. We seek to encourage the creation of algorithms capable of interpreting complex scenarios involving multiple actors or elements, enduring adverse environmental conditions, and resisting technological threats such as cyber and adversarial attacks. Our goal is to enhance the reliability, efficiency, and adaptability of surveillance systems.
To gather further insights in video anomaly detection spanning multiple real-world scenarios, we welcome articles addressing, but not limited to, the following themes:
Interaction anomalies involving multiple objects or actors
Challenges faced by moving cameras, including motion-induced anomalies
Anomaly detection tailored for autonomous vehicles
Algorithmic robustness against diverse weather conditions
Solutions for noisy video streams, including low resolution and connection issues
Cybersecurity measures and resilience against network-based attacks
Defense strategies against adversarial machine learning techniques
Approaches to continual and lifelong learning in anomaly detection
Integration of multimodal data inputs to enhance detection accuracy
Development of efficient computational frameworks and dedicated datasets
This Research Topic aims to foster the development of innovative video anomaly detection techniques that address multifaceted real-world surveillance challenges. We seek to encourage the creation of algorithms capable of interpreting complex scenarios involving multiple actors or elements, enduring adverse environmental conditions, and resisting technological threats such as cyber and adversarial attacks. Our goal is to enhance the reliability, efficiency, and adaptability of surveillance systems.
To gather further insights in video anomaly detection spanning multiple real-world scenarios, we welcome articles addressing, but not limited to, the following themes:
Interaction anomalies involving multiple objects or actors
Challenges faced by moving cameras, including motion-induced anomalies
Anomaly detection tailored for autonomous vehicles
Algorithmic robustness against diverse weather conditions
Solutions for noisy video streams, including low resolution and connection issues
Cybersecurity measures and resilience against network-based attacks
Defense strategies against adversarial machine learning techniques
Approaches to continual and lifelong learning in anomaly detection
Integration of multimodal data inputs to enhance detection accuracy
Development of efficient computational frameworks and dedicated datasets
Keywords: video understanding, anomaly detection, video surveillance, computer vision, machine learning
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.
