Original URL:
Http://www.cnblogs.com/laiqun/p/6501865.html
The task of image target detection has made great progress in the past three years, and the detection performance has been significantly improved. But in video surveillance, vehicle-assisted driving and other fields, video-based target detection has a wider range of needs. Because of the problems such a
"Reprint" ILSVRC2016 Target Detection task review: Video target detection (VID)
Reproduced from: http://geek.csdn.net/news/detail/133792The task of image target detection has made great progress in the past three years, and the detection performance has been greatly improved. But in the field of video surveillance, veh
This is an introduction to the face detection of technology from the view of the article:
"2016 ACM MM unitbox:an Advanced Object Detection Network".
The code should not be put out, but the implementation is relatively simple. (Interrupt a sentence, the paper said speed can reach 12fps, I'm a little panic, we look at science does not) —————————— split line —————————— Introduction
Target
first, the paper
Articles in the review category:
[1] D. Geronimo, and A. M.lopez. vision-based pedestrian Protection Systems for Intelligent vehicles, book, 2014.
[2] P.dollar, C. Wojek,b Schiele, et al pedestrian detection:an evaluation of the state of the art [J]. IEEE transactions on Patternanalysis andmachine Intelligence, 2012, 34 (4): 743-761.
[3] Cogs Zhi, Li Shaozi, Chen Shuyuan, etc. Survey of pedestrian detection technology [J]. Journal of
Estimating the distribution of P (x)--Density estimationWe have a sample of M, each sample has n eigenvalues, each of which obeys different Gaussian distributions, and the formula in the assumption that each feature is independent, the effect of the formula is good, regardless of whether each feature is independent. The formula for the multiplication is expressed as shown.Estimating The distribution of P (x) is called the density estimation problem (density estimation)Anomaly
Deep learning target detection (object detection) series (i) r-cnnDeep learning target detection (object detection) series (ii) spp-netDeep learning target detection (object detection) series (iii) Fast R-CNNDeep learning target
I have roughly translated some of the articles, may have misunderstood the place, please correct me. It is worth mentioning that the debate in the comments section is also worth seeing.
Feature Detection
At first, the front-end engineers objected to browser testing, which they thought was bad because it was not a future-oriented code and could not adapt to new browsers. A better approach is to use feature detec
OpenCV provides four different edge detection operators, or High-pass filters: Sobel,scharr and Laplacian operators and canny operators, the specific detection steps are as follows: image filtering : The algorithm of edge detection is based on first and second derivative of image gray value, but the derivative is usually sensitive to noise, so it is necessary to
http://blog.csdn.net/happy_stars_2016/article/details/52691255
First, lane line detection steps
1, Grayscale
Grayscale key functions: Cvcvtcolorcvcvtcolor (iplimage* src, iplimage* DST, Cv_brg2gray);The last parameter is Cv_brg2gray, which indicates that the BRG picture (color picture) is converted to a grayscale picture (black and white).
2. Binary Value
Image binary is to set the gray value of the pixel on the image to 0 or 255, that is, the enti
Reprinted from: http://blog.csdn.net/cv_family_z/article/details/52438372
https://www.arxiv.org/abs/1608.08021
In this paper, a variety of target detection for the problem, combined with the current technical achievements, to achieve a good result.
We obtained solid results on well-known object detection benchmarks:81.8% MAP (mean average precision) on VOC2007 and 82. 5% MAP on VOC2012 (2nd place) while tak
Computer now has become a daily necessities of life, then how much do you know about computers? The following small series for you to organize some common computer hardware knowledge, quickly to learn!
Computer hardware, including all the physical parts of a computer, to differentiate between the data it contains or executes and the software that provides instructions for the hardware to complete the task. Computer hardware mainly includes: chassis, motherboard, bus, power, hard disk, storage c
Hough Forest target detection is a more fashionable target detection algorithm, Juergen Gall is proposed on 2009 CVPR.Hough Forest sounds like a combination of Hough transformation +random forest, in fact, not exactly. It is more like the combination of decision forest and regression forest plus generalized Hough Transform: Each tree in the forest is not a classification tree or a regression tree, but each
motion detection (foreground detection) (i) ViBe
Zouxy09@qq.com
Http://blog.csdn.net/zouxy09
Because of monitoring the development of the demand, the current prospects of the research is still many, there have been many new methods and ideas. The personal understanding of the following is probably summed up as follows:
Frame difference, background subtraction (GMM, CodeBook, sobs, Sacon, VIBE, W4, multi-fr
DDoS attacks are essentially time-series data, and the data characteristics of t+1 moments are strongly correlated with T-moments, so it is necessary to use HMM or CRF for detection! --and a sentence of the word segmentation algorithm CRF no difference!Note: Traditional DDoS detection is directly based on the IP data sent traffic to identify, through the hardware firewall. Big data scenarios are done for sl
The Windows Phone operating system can take actions to reduce power consumption on the device when it detects that the user or the current application is in an idle state. depending on the type of application you are creating, you may need to disable idle detection for users or applications. this topic explains how to modify the operating system's idle behavior.
Important Note:
This Windows Phone feature has certification requi
Source of Self Blog
http://www.yingzinanfei.com/2017/02/01/moxingjiancegongjuhuizong/
Model Detection Tool for formal specification language
The SMV (Symbolic model Verifier) symbol Model Detection Tool SMV is used to detect whether a finite state system satisfies a CTL formula. Its modeling mode is in modules, modules can be synchronized or asynchronous combination, module description of the basic elements
ViBe algorithm: vibe-a powerful technique for background detection and subtraction in video sequencesJudge Net: http://www2.ulg.ac.be/telecom/research/vibe/Describe:Vibe is a pixel-level video background modeling or foreground detection algorithm, the effect is better than several well-known algorithms, the hardware memory footprint is also low.Code:The algorithm executes the efficiency test program, the Wi
[OpenCV Getting Started Guide] Article 7 Line Segment Detection and Circle Detection
The Section 5 contour detection in [OpenCV Getting Started Guide] and section 6 contour detection in [OpenCV Getting Started Guide] Explain the contour detection of OpenCV. This article desc
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Summary of Pedestrian detection in PAMI in 2012:Pedestrian detection an evaluation of the state of the artPiotr dollarThis article compares many of the latest Pedestrian detection algorithms.. This paper is referred to as pami2012
Pedestrian detection a
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