Dell products l.p. (20240111868). DELAYED INFERENCE ATTACK DETECTION FOR IMAGE SEGMENTATION-BASED VIDEO SURVEILLANCE APPLICATIONS simplified abstract
Contents
- 1 DELAYED INFERENCE ATTACK DETECTION FOR IMAGE SEGMENTATION-BASED VIDEO SURVEILLANCE APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DELAYED INFERENCE ATTACK DETECTION FOR IMAGE SEGMENTATION-BASED VIDEO SURVEILLANCE APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DELAYED INFERENCE ATTACK DETECTION FOR IMAGE SEGMENTATION-BASED VIDEO SURVEILLANCE APPLICATIONS
Organization Name
Inventor(s)
Pablo Nascimento Da Silva of Niterói (BR)
Hugo de Oliveira Barbalho of Rio de Janeiro (BR)
Roberto Nery Stelling Neto of Rio de Janeiro (BR)
DELAYED INFERENCE ATTACK DETECTION FOR IMAGE SEGMENTATION-BASED VIDEO SURVEILLANCE APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240111868 titled 'DELAYED INFERENCE ATTACK DETECTION FOR IMAGE SEGMENTATION-BASED VIDEO SURVEILLANCE APPLICATIONS
Simplified Explanation
The patent application describes a method for dynamically monitoring image portions classified by a segmentation model in a video surveillance system to detect and respond to attacks on the system.
- The method involves monitoring a stream of image portions classified by a segmentation model.
- The image portions are evaluated to determine if an attack is occurring or has occurred.
- If an attack is detected, a remedial action is implemented to address the attack.
Potential Applications
This technology could be applied in various security systems, such as in airports, banks, and government buildings, to enhance threat detection and response capabilities.
Problems Solved
This technology helps in early detection of attacks on video surveillance systems, allowing for timely response and mitigation of security threats.
Benefits
The benefits of this technology include improved security measures, reduced response time to security incidents, and enhanced protection of sensitive areas.
Potential Commercial Applications
Commercial applications of this technology could include selling it to security companies, government agencies, and businesses looking to enhance their surveillance systems.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms in video surveillance systems to detect anomalies and threats in real-time.
Unanswered Questions
How does this technology handle false alarms in attack detection?
The method does not specify how false alarms in attack detection are addressed and minimized to prevent unnecessary remedial actions.
What is the scalability of this technology for large-scale surveillance systems?
The scalability of this technology for large-scale surveillance systems is not discussed, raising questions about its effectiveness in handling a high volume of image portions in real-time.
Original Abstract Submitted
one example method includes dynamically monitoring a stream of image portions that have been classified by a segmentation model of a video surveillance system, evaluating the image portions, based on the evaluating, determining that an attack on the video surveillance system is occurring, or has occurred, and implementing, or causing the implementation of, a remedial action with regard to the attack. the image portions may be image portions that have been classified by a segmentation model.
