2017-06-05 08:32:59 -0600 | received badge | ● Supporter (source) |
2017-06-05 08:24:12 -0600 | commented answer | LSVM translation invariant hey, 1. Sliding window applied is only for illustration of variance output for different ROI/input, LatentSVM in opencv already applies sliding window of the input image so I am not trying to apply sliding window in here. 2. My main interest is to detect people in general not pedestrian as there are far more structure and deformation in general person detector than in pedestrian (for example DPM achieves 88% with INRIA pedestrian but 50% with VOC person class) 3. Finally, I am interested on a non-deep learning method .. LSVM/DPM method is as far as I know is the state of the art classical method, do you know of any high accuracy classical method? Thanks for taking the time |
2017-06-05 07:24:41 -0600 | received badge | ● Student (source) |
2017-06-05 02:31:39 -0600 | received badge | ● Editor (source) |
2017-06-04 03:42:31 -0600 | asked a question | LSVM translation invariant Hi, I am using LatentSVM in opencv 2.4 on models from opencv_extra as suggested here. This is a voc sample Test image, the results with 1 different shift
I noticed different scores and bounding boxes each 1-pixel shift, what are the causes of this variation although the object is fully present in these cases? Here is the code
Edit 1 Here are important more in details points:
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