QA Official

Dynamic Video Target Detection and Tracking Technology (Getting Started) 2019-04-16
Dynamic Video Target Detection and Tracking Technology The traditional TV monitoring technology can only achieve the function of "clairvoyance", transmitting remote target images (original data) to the monitoring center, and the monitoring personnel can make judgment on the scene according to the visual video images.The purpose of intelligent video surveillance is to convert the original video data into a sufficient amount of "useful information" for the monitoring personnel to make decisions, so that the monitoring personnel can understand the events in a timely and comprehensive manner: "

Image Classification and Detection for Deep Learning: Papers Cited Most in 2010-2016 2019-04-16
Recently, I saw an article that counted the most frequently cited in-depth study papers from 2010 to 2016. During the postgraduate period, the direction was object detection, so I intercepted some papers related to the field.Personally, I think that object detection can be divided into many detailed studies. It is a good job to find the pain points and propose the available solutions for any detail, including: the understanding of convolution neural network model (theory/network structure);Behavioral analysis of structural parameters (optimization/regularization);Performance in different data distributions.

Image Recognition Paper Notes 2019-04-16
"Summary of Facial Expression Recognition Based on Deep Learning and Traditional Machine Learning" The existing face recognition technology is limited to the traditional machine learning algorithm. Under the conditions of light intensity, occlusion, posture transformation, etc., the traditional machine learning algorithm has poor robustness and is difficult to be applied to real life. In the late 1970s, Suwa and others marked the continuous sequence of face images into 20 feature points, and realized the recognition of facial expressions through the comparison of these feature points.

Implementation of K Nearest Neighbor (KNN) Classifier Based on TensorFlow —— Taking MNIST as an Example 2019-04-16
KNN classification principle Please refer to relevant articles: KNN code for TF def load_mnist_data(filename,isbatch=0,train_nums=1000,test_nums=200): from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets(filename, one_hot=True) #2、批量获取样本 if isbatch==1: X_train,Y_train = mnist.train.next_batch(train_nums) X_test,Y_test = mnist.test.next_batch(test_nums) return X_train,Y_train,X_test,Y_test else: #1、获取全部样本 X_train = mnist.train.images[0:20000] #[1:10] X_test = mnist.test.images[0:300] Y_train = mnist.train.labels[0:20000] Y_test = mnist.test.labels[0:300] return X_train,Y_train,X_test,Y_test def KNN_Classifier(X_train,Y_train,X_test,Y_test,K=5,dims=784,dist_metric='L1'): # 计

Overview of Moving Target Tracking Algorithms 2019-04-16
Moving Target Tracking is an indispensable link in video surveillance system.In specific scenes, there are some classical algorithms that can achieve better target tracking effect.This paper introduces general target tracking algorithms, compares several commonly used algorithms, and introduces particle filter algorithm and contour-based target tracking algorithm in detail.Finally, it briefly introduces the processing of target occlusion, multi-camera target tracking and target tracking under camera motion. 1. General Target Tracking AlgorithmGenerally,

Overview of Target Tracking Algorithms 2019-04-16
General Classification of Mainstream Algorithms for Moving Target Tracking is mainly based on two ideas: a) Without relying on prior knowledge, moving targets are directly detected from image sequences, and target recognition is carried out to finally track the moving targets of interest;B) depending on the prior knowledge of the target, firstly modeling the moving target, and then finding the matching moving target in real time in the image sequence.

Prospect of Target Tracking Algorithm 2019-04-16
This article is the blogger's personal opinion and summary in the process of learning target tracking. Welcome to learn from and exchange. If there are any errors, please leave a message to point out. In the process of learning, I have been thinking about the essence of "tracking" is What? In "New Ideas of Object Tracking [VALSE Advanced Technology Selection and Introduction 16-18]", Dr. Wang Naiyan proposed that the essence of tracking should be verification, rather than the current mainstream tracking by detection/classification.

Public Data Set Download in Various Fields 2019-04-16
Public Data Set Download in Various Fields I found the collation of a data set on the Internet and turned it around without searching.Original address: Finance U.S. Department of Labor Statistics Official Release of Data Real Estate Company Zillow Publishes Historical Data on US Real Estate Full data of Shanghai, Shenzhen, stock divestiture, without dividend and rights issue issuance as of December 31, 2016 Daily data of Shanghai's main board, as of May 05, 2017, the original price, the former composite price, the After the resumption of the exercise price, 1260 shares

Summary of Classical Papers on Digital Image Processing 2019-04-16
colorization and colortransfer Semantic Colorization with InternetImages, Chia et al. SIGGRAPH ASIA 2011 Color Harmonization , Cohen-Or,Sorkine, Gal, Leyvand, and Xu. Web Page Computing the alpha-Channel with ProbabilisticSegmentation for Image Colorization, Dalmau-Cedeno, Rivera, and Mayorga Bayesian Color Constancy Revisited,Gehler, Rother, Blake, Minka, and Sharp Color2Gray: Salience-Preserving Color Removal, Gooch, Olsen,Tumblin, and Gooch Color Conceptualization, Hou and Zhang Light Mixture Estimation for Spatially VaryingWhite Balance, Hsu, Mertens, Paris, Avidan, and Durand.

Summary of Target Tracking Theory and Methods 2019-04-16
Reprinted from: 1. Introduction: How can the target entering a specific area be judged and the track of the target be tracked in the environment inside to be monitored?There are two situations: one is target tracking under static background;The second is target tracking under dynamic background. 2. Target tracking method under static background 1. Single target: Target tracking can also be divided into single target tracking and multi-target tracking.