There is no one-size-fits-all solution for video analytics in perimeter protection. Every deployment environment is unique, and there may be objects that are not covered in the training samples. Hikvision has developed a technology that enables an algorithm to learn and improve in the actual deployed environment.
There is no one-size-fits-all solution for video analytics in perimeter protection. Every deployment environment is unique, and there may be objects that are not covered in the training samples. Hikvision has developed a technology that enables an algorithm to learn and improve in the actual deployed environment.
The technology, Self-Learning Analytics, features the self-supervised learning (SSL) paradigm in machine learning. It doesn't rely on manual labeling, enabling video analytics appliances to adapt to the dynamics within the scene autonomously.
Self-Learning Analytics incorporates an inference engine in an NVR to automatically identify and annotate false alarms generated in the initial round of analysis. It periodically takes that information to train and deploy a new algorithm in the NVR.
Over time, the algorithm becomes better at distinguishing between people, vehicles, and other objects, especially those distinct objects in the deployment environment.
The technology, Self-Learning Analytics, features the self-supervised learning (SSL) paradigm in machine learning. It doesn't rely on manual labeling, enabling video analytics appliances to adapt to the dynamics within the scene autonomously.
Self-Learning Analytics incorporates an inference engine in an NVR to automatically identify and annotate false alarms generated in the initial round of analysis. It periodically takes that information to train and deploy a new algorithm in the NVR.
Over time, the algorithm becomes better at distinguishing between people, vehicles, and other objects, especially those distinct objects in the deployment environment.
How does Self-learning Analytics works？
The first analysis
(Tagged by persons, vehicles, or others)
Sample Collection
The second analysis
Self-iteration
Self-annotate
Self-training
Alarm accuracy improved
Inference process
Secondary analysis of all the reported images
Training process
It uses samples from specific scenario conditions to train and deploy new algorithms
Inference and training work simultaneously in NVR, iterating algorithms according to scenarios.
The power of Self-Learning Analytics
The algorithm iterates automatically after the deployment, with no effort needed from the user.
The algorithm adapts to the particular conditions of a scenario with growing detection accuracy.
Running in the NVR, the feature supports application across all connected video channels and is compatible with conventional non-AI cameras.
The algorithm iterates automatically after the deployment, with no effort needed from the user.
The algorithm adapts to the particular conditions of a scenario with growing detection accuracy.
Running in the NVR, the feature supports application across all connected video channels and is compatible with conventional non-AI cameras.
Which products come equipped with Self-Learning Analytics?
Recommended Product Solutions
Network Camera with AcuSense
K-VPro Series NVRs
- Self-iterative algorithms to improve alarm accuracy
- Reduce manual maintenance costs to save labor and effort
Residences
Warehouses
Network Camera with AcuSense
M-VPro Series NVRs
- Self-iterative algorithms to improve alarm accuracy
- Reduce the workload and increase the efficiency of security staff
Factories
Industrial parks
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