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As AI-based video analytics continues to revolutionise industries, a hidden threat lurks in the shadows, compromising the accuracy and reliability of these systems. Catastrophic interference, also known as forgetting, occurs when neural networks forget previous knowledge during the learning process, leading to a decline in performance. In this article, I’ll explore the implications of catastrophic interference on AI-based video analytics and how IntelexVision can help mitigate this risk.
The problem: catastrophic interference in neural networks
Catastrophic interference occurs when a neural network is trained on new data, causing it to forget previously learned information. This phenomenon can lead to:
- Decreased accuracy
- Reduced performance
- Inconsistent results
In AI-based video analytics in surveillance, catastrophic forgetting can result in:
- Missed detections
- False positives
- Inaccurate object recognition
The general impact on AI-based video analytics
Catastrophic interference can have severe consequences in various applications, not only CCTV, including for example:
- Healthcare: Inaccurate object recognition can result in misdiagnosis
- Retail: Inconsistent results can lead to inventory management issues
The impact on anomaly detection
In anomaly detection, neural networks are constantly trained to identify unusual patterns and behaviours. However, this continuous training can exacerbate catastrophic interference, leading to:
- Decreased detection accuracy
- Increased false positives
- Missed anomalies
As the network forgets previous knowledge, it becomes less effective at detecting anomalies, compromising the entire system.
The IntelexVision solution
IntelexVision offers a solution to mitigate catastrophic interference in its unusual behaviour detection:
- Scheduled training: IntelexVision’s algorithms allow for scheduled training periods, ensuring that the neural network is only trained during set times. By scheduling training periods, IntelexVision helps maintain the neural network’s ability to detect anomalies accurately, ensuring the reliability of the system.
- An alternative option is to use multiple neural networks and switch between them in the time domain to prevent forgetting.
This approach helps avoid catastrophic forgetting by:
- Reducing the frequency of training
- Allowing the network to consolidate previous knowledge
- Preventing the overwriting of existing knowledge
Other techniques useful to mitigate the risk of catastrophic forgetting
- Regularisation techniques: Implementing regularisation techniques, such as L1 and L2 regularisation, can reduce the impact of catastrophic interference.
- Knowledge distillation: Transferring knowledge from a pre-trained model to a new model can help retain previous knowledge.
- Data augmentation: Increasing the diversity of training data can reduce the likelihood of forgetting.
- Ensemble learning: Combining multiple models can improve overall performance and reduce the impact of catastrophic interference.
Conclusion
Catastrophic interference poses a significant threat to AI-based video analytics, especially in solutions providing anomaly detection with continuous learning. IntelexVision offers solutions to mitigate this risk. By implementing matured products, you can ensure the accuracy and reliability of your AI-based video analytics systems. Don’t let forgetting hold you back – future-proof your AI today!
Learn more about IntelexVision’s solutions and how to overcome catastrophic interference in AI-based video analytics. Contact us to schedule a demo and ensure the accuracy and reliability of your AI systems.