Call me Prometheus

This last week I finished creating the training and testing sets of positive fire videos. I used "Windows Movie Maker" to cut and cleanup the raw fire videos I'd taken over winter break. The result was a bunch of .wmv files which I then converted to .avi files using "STOIK Video Converter 2" because this is the format that OpenCV seems to like.

Next I read through All of my research material on video-based fire detection. The following is a list of the different techniques and algorithms that were used to detect fire in video:

1. Detect fire colored pixels using gaussian-smoothed color histograms
2. Detect the temporal variation of fire pixels by computing the average pixel-by-pixel intensity difference for fire pixels and for non fire pixels and subtracting the latter from the former.
3. Detecting smoke by noticing a decrease in the variance of the color histogram of the image caused by the increased homogeneity of the image due to smoke's blurring effect.
4. Also, observe the change in density of the smoke by observing the power spectra in the frequency domain.
5. Detect the color variation of fire pixels in the wavelet domain of fire pixels.
6. Detect the zero-crossings of the wavelet transform coefficients. This can be done in the pixel intensity range or in the R-color channel. Analysis is done in both the spatial and frequency domains using filter banks.
7. Using HMMs on the color of the target pixel.
8. Train a neural network on the H, S, and I channels of each pixel to classify the pixel as being a fire pixel or not based on the pixel color characteristics.
9. Look for the following characteristics of smoke in images (1) smoke smooths object edges and since edges correspond to extrema in the wavelet domain, a decrease in these extrema is an indicator of the presence of smoke (2) smoke Decreases the values of the image's chrominance channels.
10. Fire detection based on time derivatives. A pixel is determined to be a fire pixel based on intensity-weighted time derivatives.
11. Thresholded temporal color variation. If the temporal color variation passes a threshold then label the pixel as being a fire pixel.

I've started working on a simple pixel color based perceptron classifier as the first step in a multi feature based fire detection algorithm. More on that In my next post.