1. Target DetectionTarget detection is to extract the changed region from the background image from the sequence image.The algorithm of moving target detection can be divided into static background motion detection and dynamic background motion detection according to the relationship between the target and the camera.
1. Static background: background difference method, inter-frame difference method, optical flow method.The commonly used algorithms in opencv are absdiff,GMM (Gaussian Mixture Model), Lucas-Kanade method, etc.
Reprinted from: http://blog.sina.com.cn/s/blog_4b700c4c01017wz5.html
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.
Particle filter is widely used in target tracking. Particle filter is a sequential Monte Carlo filtering method, which essentially uses a series of randomly selected samples (i.e. particles) to replace the posterior probability distribution of states.We do not intend to introduce and reason complicated probability formulas here. Let's analyze RobHess source code to further understand the particle filter algorithm.
Test Platform: VS2010+opencv2.
2016 was an astounding year for insightful, informative data visualization articles — here were the top 10 I saw all year (in no particular order), in each case including a representative quote from the piece and a brief note of why it was at the top of my must-read list:
1 — 39 studies about human perception in 30 minutes, by Kennedy Elliott For the last several years, I’ve wondered about what we actually know from scientific studies about how humans perceive graphics.
ArtificialIntelligence, abbreviated as AI, is a new technical science that researches and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.
Artificial Intelligence is a simulation of the information process of human consciousness and thinking.However, it is not human intelligence. It can think like a human and may exceed human intelligence.But this kind of advanced artificial intelligence, which can think for itself, needs breakthroughs in scientific theory and engineering.
Simply speaking, a Convolutional Neural Network model trained on a data set is quickly moved to another data set through simple adjustment. As the number of layers of the model and the complexity of the model increase, the error rate of the model also decreases.However, to train a complicated Convolutional Neural Network requires a lot of labeling information, as well as several days or even several week. In order to
Wen/think tank 2861
Artificial Intelligence not only replaces part of our work, but also changes our way of life in many ways.The most important part lies in the improvement of efficiency.
From the perspective of economic growth in Greater Period, it basically depends on money, population and production efficiency.At present, the global population growth has gradually entered an inflection point, especially the negative population growth in developed countries.
"Remote Sensing Inversion of Winter Wheat Leaf Area Index Based on Support Vector Machine Regression _ Liangdong"
[Looking at this paper with learning SVM, the result is that a large number of biological statistics, formulas and models directly confuse people, instead, they cannot see what they really want to see, but they cannot read it for nothing, so they have to write their own thoughts]
1. What is this article for
Type 1: General Test
By means of performance test, the test that simulates low or no concurrency to the system and does not cause pressure to the system is a general performance test.The main purpose is to verify whether the system can meet the performance index requirements under normal conditions.For example, two login systems, if the login time of the system is 8 seconds, then there is no need to carry out load test for this system, because it can not meet the requirements even in general.
I wrote a C++ version of the binary image connected region labeling function before, and the intuitive result at that time was no problem. I also used it for a long time, only to find that the result was wrong, I'm so sorry!
What is posted here is an improved binary image connected region labeling function. Currently, only 4 connected region labeling is supported. To achieve 8 connected region labeling, the simplest method is to dilate the input image (each row) with [1 1 1].