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Using the create samples application I can create an arbitrary amount of positive images from a small set (whilst not always the best option, it does save time). My first question is ideally what should the ratio be between positive and negative images (including the samples generated) - and does that affect the run time of opencv_train cascade. Im currently running a trainer with POS count = 1000, NEG count acceptanceRatio 48:1.

My other questions are about the quality and size of negative images. Is there any advantage to having high resolution (several MB) negatives if the ultimate application of the detector is a Kinect camera? Also, does it matter what the negative images are? I just used random holiday photos for my first test and the quality of my detector wasnt brilliant.

Using the create samples application I can create an arbitrary amount of positive images from a small set (whilst not always the best option, it does save time). My first question is ideally what should the ratio be between positive and negative images (including the samples generated) - and does that affect the run time of opencv_train cascade. Im currently running a trainer with POS count = 1000, NEG count acceptanceRatio 48:1.48:1. It has been on training stage 0 for a while now.

My other questions are about the quality and size of negative images. Is there any advantage to having high resolution (several MB) negatives if the ultimate application of the detector is a Kinect camera? Also, does it matter what the negative images are? I just used random holiday photos for my first test and the quality of my detector wasnt brilliant.

Using the create samples application I can create an arbitrary amount of positive images from a small set (whilst not always the best option, it does save time). My first question is ideally what should the ratio be between positive and negative images (including the samples generated) - and does that affect the run time of opencv_train cascade. Im currently running a trainer with POS count = 1000, NEG count acceptanceRatio 48:1. It has been on training stage 0 for a while now.750:1.

My other questions are about the quality and size of negative images. Is there any advantage to having high resolution (several MB) negatives if the ultimate application of the detector is a Kinect camera? Also, does it matter what the negative images are? I just used random holiday photos for my first test and the quality of my detector wasnt brilliant.

Using the create samples application I can create an arbitrary amount of positive images from a small set (whilst not always the best option, it does save time). My first question is ideally what should the ratio be between positive and negative images (including the samples generated) - and does that affect the run time of opencv_train cascade. Im currently running a trainer with POS count = 1000, NEG count acceptanceRatio 750:1.

My other questions are about the quality and size of negative images. Is there any advantage to having high resolution (several MB) negatives if the ultimate application of the detector is a Kinect camera? Also, does it matter what the negative images are? I just used random holiday photos for my first test and the quality of my detector wasnt brilliant.