Data Imbalance in PSNR Dataset for BAA: Challenges and Solutions

The PSNR dataset used in BAA (Behavioral Analysis and Anomaly Detection in Video) may suffer from data imbalance. This means that there may be significantly more data points for one class compared to another class, leading to biased training of the machine learning model.

For example, if the dataset has more normal video frames than anomaly video frames, the machine learning model may become better at detecting normal frames but may struggle to identify anomaly frames accurately. This can lead to false positives or false negatives in the anomaly detection process.

To address this issue, several techniques can be used, such as oversampling the minority class, undersampling the majority class, or using a combination of both. Another approach is to use data augmentation techniques, such as flipping, rotating, or adding noise to the minority class data to increase its size and diversity.

It is important to carefully evaluate the performance of the machine learning model on both the majority and minority classes and to choose appropriate evaluation metrics that take into account the data imbalance.