This example aimed to provide a good classifier that was able to recognise a flat contract palm in videos, but the results, as demonstrated in the haar performance evaluation, turned out quite disappointed. Here I list the details of this example, including the first stage of sampler extract, creating samples, training samples, and the final evaluation.
Note that this example was a very typical in terms of hand gesture recognition – it followed good procedural flows of haartraining and evaluation, but with low level accuracy of hand gesture recognition in video streams.
What this example aimed to do?
So far when I was typing these words there were still very few functions provided by openCV to be able to recognise natural hand gestures accurately, some laboratory demos may should improved recognition performance though.
For this reason, this example was proposed to provide a good hand gesture recognition function that was based on openCV haartraining. As a good starting point to detect complicated hand gestures, the example focused on flat contract palm gesture.
The example started from collecting a good number of gesture pictures, as required by haartraining, both positive pictures and negative pictures were collected with 4,000-5,000 pictures each category. A simple picture sampler was described in one prior article here haartraining sample picture collector.
all haartraining done until here.
Since the original outcomes of the haartraining were .txt files in respective folders, it needed a converter to produce .xml file for test use. The txt2xml converter can be found her haar converter.
Finally the test programme to see how the trained haar classifier worked with video streams. The test codes can be found here hand gesture haar test.
and the final outcomes:
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