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I am using OpenCV sample code “peopledetect.cpp” to detect pedestrians. The code uses HoG for feature extraction and SVM for classification. Please find the reference paper used here.

The camera is mounted on the wall at a height of 10 feet and 45o down. There is no restriction on the pedestrian movement within the frame. I am satisfied with the true positive rate (correctly detecting pedestrians) but false positive rate is very high.

Some of the false detections I observed are moving car, tree, and wall among others. Can anyone suggest me how to improve the existing code to reduce false detection rate. Any reference to blogs/codes is very helpful.

2vision2
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You could apply a background subtraction algorithm on your video stream. I had some success on a similar project using BackgroundSubtractorMOG2.

Another trick I used is to eliminate all "moving pixels" that are too small or with a wrong aspect ratio. I did this by doing a blob/contour analysis of the background subtraction output image. You need to be careful with the aspect ratio to make sure you support overlapping pedestrians.

Note that the model you're using (not sure which) is probably trained on a front faced pedestrian and not with a 45 degrees angle down. This will obviously affect your accuracy.

rold2007
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