hog silhouette

After opencv2, hog-related content was added and an example was provided, using the method first proposed by French navneet Dalal at cvpr2005. Firstly, hog is used to perform people detection. A complete method has been provided. In peopledetect. CPP, the main methods include hog feature extraction, training, and recognition. You can useHog. setsvmdetector (hogde

Hog has many similarities with the edge histogram, scale-unchanged Feature Transform (SIFT), and shape context (shape contexts), but they differ in the following ways: the hog descriptor is calculated on a grid-intensive Cell Unit with uniform sizes. To improve performance, overlapping local contrast normalization is also adopted. the hog method operates on the l

each match point between the cost and minimum. The results of the match are as follows (Figure III): (Figure III) The above method does not use machine learning, the other part of the search is not an easy thing, because the first to approximate the location of the component, so this method also has shortcomings, but the idea of the deformed part can be used as a feature, and then look at Pedro's second article [2] how to use it for target detection. Pedro in the literature [2] the target dete

again. 5. Learning and improving classifiers Now if the sample number is more, feature selection and classifier algorithm are better, the detection accuracy of the classifier is very high. But it will also be the wrong time to check. So the more advanced point is to join the study or self-adaptation, that is, you put this picture classification error, I will take this picture out, labeled its correct category, and then put in the sample library to train the classifier, let the classifier update

Common Methods for moving object detection include optical flow, background subtraction, and frame difference ). The Background Subtraction Method and the interframe difference method are suitable for static cameras, while the optical flow method is used for camera motion, but the calculation is relatively large. The following describes how to use the cvupdatemotionhistory function in opencv to detect motion targets. Cvupdatemotionhistory is used to update motion history images. Its prototype is

1, choose a picture of your favorite star. 2, enhance the reflection, and then use the color level to enhance color contrast. Use the color-deepening tool to smear the top of the sky patiently. 3, find a sunrise wallpaper dragged in, put a good position. 4, add layer mask, with a black brush to erase the sun above the part of the smooth background of th

Introduction to Clustering algorithm and cluster evaluation Silhouette Introduction to Clustering algorithms Clustering (clustering) is a type of unsupervised learning (unsupervised learning) that divides a set of data into several categories, with the data in each class as similar as possible, while maximizing the differences between classes. By clustering, you can provide references for sample selection, root cause analysis, or as a preprocessing

is basically based on the HOG+SVM Pedestrian detection algorithm published by French researchers Dalal in the 2005 CVPR (histograms of oriented gradients for Human Detection, Navneet Dalel,bill Triggs, CVPR2005). HOG+SVM as a classic algorithm is also integrated into the OPENCV inside, can be directly called to implement pedestrian detection In order to solve the speed problem, we can use the background di

The Code improvements in opencv, of course, should be based on their own actual situation, opencv Pedestrian detection has two kinds of Rectangular Box fusion algorithm, here only to improve the meanshift Method If you have a better method, I hope you can share it with me. I have also made improvements for removing the coincidence part, and can add it to my own program. Why do I need to perform local meanshift? Figure 1. Global meanshift : The two images are close to each other and have multipl

Original URL: Http://www.bo-yang.net/2014/04/30/fisher-vector-in-action-recognition This is a summary of doing human action recognition using Fisher vectors with (improved) dense trjectory Features (DTF, HTTP ://lear.inrialpes.fr/~wang/improved_trajectories) and STIP features (http://crcv.ucf.edu/ICCV13-Action-Workshop/ download.html) on UCF 101 DataSet (http://crcv.ucf.edu/data/UCF101.php). In the STIP features, the low-level visual features HOG an

OpenCV Handwriting Selection quiz (b) Character recognitionThe choice question basically only need to recognize ABCD and empty five content, theoretically should recognize the rate is higher, the identification code refers to the online search code, because the reference URL is more, now also can not clear is the reference where the code, here does not thank each.Basic steps:First, the recognition function accepts the gray image of General 64x64;Second, binary and inverse color for white on blac