accelerometer motion detection algorithm

Human Motion Detection refers to the process of moving the human body in the input video images, including the position, scale, and posture, Human body tracking is the process of determining the human body correspondence between frames in a video image sequence. A series of processing methods such as low-pass filtering, background difference, morphological image processing, and Region connectivity analysis

module cvfgdetector: its input data is the current frame image, and the output data is the foreground image (mask) of the current frame image ). The foreground image is a binary image of the same size as the input video frame. That is, if the pixels in the current frame are regarded as the moving foreground, the pixel value in the corresponding position in the foreground mask is 1. Otherwise, the corresponding pixel value is 0. Developers must inherit the cvfgdetector class and implement pure v

Human motion recognition system based on Kinect (both algorithms and codes are released) first of all, the development environment version used by this system is the computer systems Windows 10, Visual Studio 2013, Opencv3.0, and the Kinect SDK v2.0. These can be found on Baidu, download down to install it. For the Kinect environment configuration and bone data acquisition and so on, refer to my previous Kinect series blog (http://blog.csdn.net/baol

Algorithm for absolute static areas in the image to improve the vertical resolution. For absolute motion areas in the image, use the intra-field interpolation algorithm, improves the time-domain resolution and delivers a good effect in fast motion scenarios. When an image is in an absolute static or absolute

CAMSHIFT tracks the center and size of the probability distribution of an object, it is only as good as the probability distribution that you produce for the object. typically the probability distribution is derived from color via a histogram, although it cocould be produced from correlation, recognition scores or bolstered by frame differencing or motion detection schemes, or joint probabilities of differ

I. Introduction to the detection of moving targetsMoving object detection in video this piece of the present method is too much. The algorithm of moving target detection according to the relationship between target and camera can be divided into static background motion

The research of image feature detection has been a long time, many methods of image feature detection, coupled with the deformation of various algorithms, it is difficult to fully understand in a short period of time, but the main feature detection algorithm principle of the study. Overall, image features can include c

movements of certain objects in a scene, such as swaying branches and leaves, fluctuations in the water surface, etc. 3, initialization problem. In some monitoring scenarios, it is difficult to get a pure background image without noise interference (images without detection targets and motion background targets). For example, the busy traffic scenes of people's cars. 4, occlusion and hole problems. To dete

on the effect. For each frame to be detected, the algorithm of this paper has multi-scale detection. For example, a car is 30*30, if the 24*24 window to detect is not detected, so when we reduce the picture 0.8 times times to become 24*24, it can be detected. However, we can still use the 30*30 rectangle to frame the vehicle in the original image. Because we are reduced to 0.8 times times the image of the

hist2=zeros (1,4096); forI=1: A forj=1: b q_r=fix (Double(Temp1 (I,j,1))/ -); Q_g=fix (Double(Temp1 (I,j,2))/ -); Q_b=fix (Double(Temp1 (I,j,3))/ -); Q_TEMP1 (I,J)=q_r* the+q_g* -+Q_b; Hist2 (Q_TEMP1 (i,j)+1) = Hist2 (Q_TEMP1 (i,j) +1)+M_wei (I,J); End End Hist2=hist2*b; %figure (2); %subplot (1,2,1); %plot (HIST2); %Hold on ; W=zeros (1,4096); forI=1:4096 if(Hist2 (i) ~=0) %Not equal to W (i)=sqrt (Hist1 (i)/Hist2 (i)); ElseW (i)=0; End End%initialization of variables Sum_w=0; XW=

Not all, need to add slowlyA Moving target detection(i) Poor background1. Frame Difference2.GMMsuch asThe background subtraction algorithm can model the illumination change, noise disturbance and periodic motion of the background, and it can detect the moving target accurately in various situations. Therefore, in the case of fixed camera, the background subtracti

Moving target detection algorithm based on local two-value similarity mode (LBSP)[Email protected]Http://blog.csdn.net/kezunhaiThis article is based on paper: improving background subtraction using local binary similarity patternsWACV2014 the content and your own understanding, if you want to learn more details, please refer to the original text. The article thought borrowed from vibe, in fact can be unders