#ANOMALY 2 BENCHMARK UPDATE#
#ANOMALY 2 BENCHMARK CODE#

In addition, wavelets can be used for a variety of other tasks such as deblurring, denoising, and edge detection. By doing so, wavelet compression can result in much smaller file sizes without compromising on quality. Wavelets can also be used to compress images, by identifying and removing unnecessary details. To get started with wavelet transforms in Python, we can use a library called PyWavelets. It can be used for image processing by decomposing an image into different frequency bands. Wavelet is a signal processing technique that can be used to decompose a signal into different frequency components. This means that the algorithm should be fast and that the feature vector that we extract from the pictures should be small–as little as 10 dimensions in our case–if we want to have a chance to train a model without spending more than a couple of milliseconds on an edge device.

#ANOMALY 2 BENCHMARK UPDATE#

We need to train the algorithm every time we update the sliding window. In our case, we could create a reference distribution out of the last 200 photos taken by the camera and compare this distribution with the new incoming pictures. In addition, we also need to obtain data for the entire range of values that covers all possible outcomes that have not been subject to anomalies.įortunately, there is a method to tackle this problem: create a sliding window and use unsupervised anomaly detection methods.

The problem with this method is that we assume that the reference distribution doesn’t change over time. In my case, we needed to identify anomalies in real-time from video images.Ī typical approach would be to build a reference probability distribution out of anomaly-free images and compute the distance between new images and the reference distribution to find out whether or not we are facing an outlier. This can be used to identify problems in a system, fraudulent behavior, or other unusual activities. Roughly speaking, anomaly detection techniques try to identify patterns in data that do not conform to typical behavior. In this post, I’ll describe how I achieved this goal and some of the challenges I encountered along the way.

#ANOMALY 2 BENCHMARK CODE#

I needed to improve the speed of my code by 10x. Recently, I’ve been working on an anomaly detection model for a project at work.

Anomaly 2 benchmark