The first research in the laboratory I participated in was to realize an algorithm of
Pedestrian detection. In feature extraction, we calculated Histogram of Oriented
Gradient (HOG), which is widely used for its effective adaptation to changes of object
pose and illumination. Then we proposed a new ensemble classifier based on
L2-norm minimization principle to detect human body from static images. The
proposed classifier can achieve margin maximization, but with less complexity
compared to the well-known Support Vector Machine (SVM) classifier. Experimental
results show that, the classifier gets a better performance in terms of detection rate,
while 6-8 times faster than SVM.
Now I am trying to setting up a similar detection method in another platform, car
detection. My current work is optimizing feature extraction from detected images.
While we in the former work set a series of detecting window in different size to
contain target in varied volumes, I am trying to change the size of detected image in
pyramid instead of detecting window. Thus I expect to improve the speed of
detection.
In further working and studying, combined with my project, I found a lot of problem
confusing me. In the extracting feature from the target image, when the pedestrian is
smaller(not much) in size than the training simple, the result will not be good enough.
I hope to solve the problem by zooming the image with method of interpolation
instead. And if more than 1/3 of body is blocked out, it won’t be detected. As for cars
detection, though the complexity of the feature seems lower, it turns out that the
similar method is less robust to the varying viewing angle and blockage. I am trying
to add the training sample to adjust these changes. These are the problems I am
solving.