keras object detection

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Some common concepts in object detection transfer learning, IOU, NMS Understanding

1. Migration Learning Migration learning is also known as supervised pre-training (supervised pre-training), which we often call migration learning. For example, you already have a bunch of picture data labeled for the age of the face, trained a CNN to recognize the age of the human face. Then when you meet a new project task is: Face sex recognition, then you can use the trained age to identify the CNN model, remove the last layer, and then the other network layer parameters are copied directly

(reproduced) using the SIFT and RANSAC algorithms (OPENCV framework) to achieve object detection and positioning, and to find the transformation matrix (Findfundamentalmat and findhomography comparison) pinned

Original link: 46914837#commentseditThe goal of this paper is to use SIFT and RANSAC algorithm, to complete the correct matching of feature points, and to find the transformation matrix, through the transformation matrix to identify the boundary of the object (the article has some source code, the whole project I also uploaded, please click here).Sift algorithm is currently recognized as the most effective feature point

"CV paper reading" + "porter" locnet:improving Localization accuracy for Object Detection + A Theoretical analysis of feature pooling in Visual recognition

function of p, and the P is extended to the real field, and the most valued point isThe function rises first and then drops, the limit is 0. Suppose that when P=1 is the desired distance of the mean, there will be a lot of p, which can make the distance increase. Suppose that, if, can be rolled out, This indicates that one of its selected features represents more than half of the patches in the image (this sentence I understand is, because that is the probability of selecting/generating feature

Ogre collision detection, accurate to the object mesh Triangle Surface

Ogre collision detection, accurate to the object mesh Triangle Surface (19:14:27) Reprinted Tags:Miscellaneous Category: MSN migration The final project of our course is a three-dimensional game of mosquito-sucking blood. Because the volume of mosquitoes is small, and mosquitoes need to fly in 3D scenarios. Therefore, the regular model detection

Opencv extends latent SVM discriminatively trained Part Based Models for Object Detection

Opencv 2.4 implements the DPM program in C ++. The main difference between it and the previous C version is that it can detect multiple targets at the same time. During use, you can put the trained model in a folder, and put the image to be detected in another folder for detection. Unfortunately, the accelerated content is not considered. Latent SVM regression ¶ Discriminatively trained Part Based Models for obje

Type of JS Detection object

In JavaScript, the most reliable way to determine what kind of built-in type an object's value belongs to is through the Object.prototype.toString method.Example:var array=[1,2,3];object.prototype.tostring.call (array); // "[Object Array]" var obj={name: ' Tom '};object.prototype.tostring.call (obj); // "[Object Object]" var string= ' Hi '; Object.prototype.toStr

Reading Paper Series: Object Detection spp-net

original RCNN, accurate rate is not badSummaryStrictly speaking spp-net is not for the detection model, but spp-net for rcnn evolution to fast-rcnn played a great role, it is worth reading. Spp-net idea is very interesting, SPP (Spatial Pyramid Pooling) is an improvement of the network structure, probably because it is Chinese writing paper, feel very good reading, gold content personal feeling no rcnn or DPM paper High, but the experiment is very ri

Topic Cascade Object Detection with deformable part models a suspicion

The author of this article is Pedro F. felzenszwalb wait a minute, famous DPM in the target detection model. The work of this article is a cascade of DPM (deformed component models) to speed up detection.The accelerated way, now followed by the calculation of certain parts of the score, assumes that the values are less than a certain threshold value, discarding the position of the object to continue to dete

SSD Object Detection Model training and test summary

Reference URL: github:https://github.com/naisy/realtime_object_detection2018.10.16SSD Object Detection Summary:Remember to take a cursory look at the notes and start training the modelError: 1, with branch1.5,tensorflow-gpu==1.8 training model in GT730, video memory 2g, can not run, tensorflow-gpu==1.5 no NoMaxSuppressionv3,2, using the pre-training model ssd_mobilenet_coco_2018_1_28,tensorflowgpu==1.5 trai

Use the sift source code of David Lowe to implement left-behind object detection

David Lowe's sift has always been used by everyone. We can't compile it by ourselves. It's not as good as we can compile it. First, use sift to extract feature points from the target object as the basis for subsequent judgment. The purpose of the demo is to detect the target object in another video. We use opencv to read a video. Use sift to extract the feature points of each frame and then perform matching

Opencv--brisk feature detection, matching and object finding

To detect and draw feature points:1#include 2#include 3#include 4 5 using namespaceCV;6 using namespacecv::xfeatures2d;7 using namespacestd;8 9 intMainintargcChar**argv) {TenMat src = imread ("test.jpg", Imread_grayscale); One if(Src.empty ()) { Aprintf"could not load image...\n"); - return-1; - } theNamedwindow ("Input Image", cv_window_autosize); -Imshow ("Input Image", SRC); - - //brisk feature point detection +ptr//creates a b

Attribute detection in a JS object

A problem was encountered in the recent project where the Window object was rendered when the page was loaded, an object obj was added to the window during rendering, a file was loaded, and an attribute para was added to obj, but the process was asynchronous.You then need to detect if the window has an Obj object, and if the Obj

Object Detection with discriminatively trained Part Based Models

and the samples on the margin are not counted as E or H. The whole is to continuously optimize SVM by selecting the samples that are difficult to distinguish, the difference between common SVM and lsvm is that the sample selection method is the same for different loss functions. The sample selection process is as follows, Let C1 d be an initial cache of examples.Algorithm repeatedly trains a model and updatesCache as follows:1) Let T: = (CT) (train a model using CT ).2) if h (T; d) CT stop and

Sliding Shapes for 3D Object detection in Depth images__ three-dimensional

Haven't written a blog for a long time. In fact, there is nothing to write yourself ... The level is limited, eh .... The robot has recently been engaged in kinematics, so the depth vision is neglected here. However, recently saw a previous article feel very interesting, so want to take out and share with you. The title of the article is titled "Sliding Shapes for 3D object detection in Depth Images", whi

Another great leap in rcnn--object Detection 2 (including sppnet, Fast rcnn)

unlike rcnn each candidate area to the depth of the network feature, but the entire map to mention a feature, and then map the candidate frame to the CONV5, Because the size of the candidate box is different, the mapping to the CONV5 is still different, so the SPP layer will need to be extracted to the same dimension of the characteristics, and then classification and regression, the following ideas and methods are consistent with RCNN. In fact, this is a lot faster than the original, because b

OpenCV, about object detection

the //Open operation (expansion corrosion operator) +Mat out; -Mat element = Getstructuringelement (Morph_rect, Size (3,3)); $Erode (Gray_diff, out, Element); $Dilate ( out, out, Element); - - //Find Outlines theMat DST = Mat::zeros ( out. Size (), cv_8uc3); -Vectorcontours;WuyiVectorhierarchy; theFindcontours ( out, contours, hierarchy, Cv_retr_ccomp, cv_chain_approx_simple); - intindex =0, Largestcomp; Wu DoubleMaxarea =0; - for(; index >=0; index = hierarchy[index][0])

Whether there is a property judgment method in JavaScript detection object summary _javascript Tips

Detection of the existence of attributes in an object can be judged by several methods. 1. Use in keywordThis method can be used to determine whether an object's own properties and inherited properties exist. Copy Code code as follows: var o={x:1}; "X" in O; True, own property exists "Y" in O; False "ToString" in O; True, is an inherited property 2. Use the hasOwnProperty ()

Control of object movement and collision detection

Using unityengine;using System.collections;public class Move:monobehaviour {gameobject go;//use this for initialization void Start () {go= gameobject.find ("C4"); Cubego.renderer.material.color = color.red named C4; Set its material to red}//update is called once per framevoid update () {//To detect in real time in each frame whether the keyboard is pressed and to control the direction of C4 movement via W, S, A, D keys if (Input.getkey (K EYCODE.W)) {go.transform.Translate (

OpenCV contour detection to calculate object rotation angle

The effect is still a bit of a problem, I hope we discuss togetherFindrotation-angle.cpp: Defines the entry point of the console application. FindContours.cpp: Defines the entry point of the console application. #include "stdafx.h" #include This is the original implementation of the Code of the blog post:http://blog.csdn.net/wangyaninglm/article/details/41864251Reference documents:http://blog.csdn.net/z397164725/article/details/7248096http://blog.csdn.net/fdl19881/article/details/6730112http://b

"Paper notes" Object contour detection with a fully convolutional encoder-decoder network

Object Contour Detection with a fully convolutional encoder-decoder network Using convolutional encoding and decoding network to detect the edges of primary targets The network structure is:Code: VGG-16Decoding: Reverse pooling-convolution-activation-dropout Convolution cores: The number of channels of every decoder layer is properlyDesigned to allow unpooling the maxpooling layer from its corresponding.

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