with MultiScale() ) regardless of the model I use, i get a pretty window right in the middle of the image :/ – Do you have any Idea of what could be the issue here?Congrats on training your own model David, that’s great. If you know how the camera is titled, simply deskew it by rotating it by theta degrees.Thanks for your answer, this is a good idea and helps for many cases. HOG features were first introduced by Dalal and Triggs in their CVPR 2005 paper, Histogram of Oriented Gradients for Human Detection. Use one or the other.Hey, Adrian here, author of the PyImageSearch blog.

persons) lead to bluring in the detector, and are not actually handled by it. Some parts failed, some works but slow, and some actually ends up being better than the reference I’m using. HOG detectMultiScale parameters explained However, I’m not sure why your detector would be falsely reporting a detection in the middle of the image each and every time. HOG Descriptor() 本次模式识别课程要求 实现 路标检测，训练集只给了5个样本，测试集有50个样本，听说 HOG 特征 + 特征 匹配就能达到很好的效果，因此采用 …

1. I’m working in a raspberry pi 2 board and the code works but the frame rate is too slow.Hey Sebastian — pleas see the “Tips on speeding up the object detection process” section. Pass 0th frame for classifier. In the original HOG paper by Dalal and Triggs, they specifically mentioned that their detector is built around pedestrian detection in allowing for significant degrees of freedom in the limbs while using strong structural hints around human body.Thanks for contributing an answer to Stack Overflow! I created this website to show you what I believe is the best possible way to get your start.Thanks again for the informative blog post. We also now have an issue of overlapping bounding boxes. I have reduced false positives.

site design / logo © 2020 Stack Exchange Inc; user contributions licensed under I’d love to hear from you; however, I have made the decision to no longer offer free 1:1 help over blog post comments.

Is this algorithm intrinsically that “fuzzy” about the precise outline, or can it be tuned to more closely match the actual boundaries of the person? What perimeters should I change? In your case, you would need to train your own SVM.I detail the steps involved in how to train a custom object detector Hi Adrian, I really need to thank you for all those amazing posts, it really is a great job!Because I liked your posts, I tried to reproduce it at home, using a Raspberry Pi 2 with Python 2.7.9 and OpenCV 3.1.0.

For In my dataset is between 2 and 6 people walking together to pass through a door with a width of 60 in (1.5m). How can I achieve this?As far as detectMultiScale goes, unfortunately there aren’t many optimizations on the Python side of things.
You can use the built-in Python help method on any OpenCV function to get a full listing of parameters and returned values. thus, if you have a trained detector (i.e.

The downside is that the HOG + Linear SVM detector now has more data to process, thus making it substantially slower.I’ve trained my own HOG detector using the command “opencv_traincascade” with the “-featureType HOG” flag on and it succefully generated a .xml file as a HOG detector.How can I implement my own XML file on the functions “cv2.HOGDescriptor() hog.setSVMDetector()” ? I would first examine the images in the dataset and determine how much variance in appearance the heads and shoulders have. from itertools import product from math import floor, pi import numpy as np import cv2 # opencv 2 def findHOGFeatures(img, n_divs=6, n_bins=6): """ **SUMMARY** Get HOG(Histogram of Oriented Gradients) features from the image. cv2. Python findFundamentalMat.

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