particle-filter

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 9 days ago . I am working on this paper. I have to apply particle filtering on it in place of markov filter. This paper is all about video anomaly. I have to implement particle filter on video. For this purpose, I need to draw samples from proposed distribution. As given that: To initialize that initial sample

问题 I am trying to extract the length and width from a set lidar sensor points (pink) as shown in the image below. The points circled in blue and white are actually noise which I wish to eliminated. [The orange box is the length and width I currently have calculated from the points. As seen, the calculated width is much 1/3 wider than it is supposed to be, due to the noisy points i blue and white] I've read some approaches to do corner/rectangle fitting, then discarding x% of the poorest fitting

问题 In my attempt to implement particle filter, I have first manually drawn a rectangle(x,y,w,h) around the car in my image (in red color), then i took 50 particles, and assigned them noise i.e x=x+noise(0,15) and y=y+noise(0,15). Then i wanted to dram all the rectangles for each particle in green color, but instead of showing 50 rectangles, it is only showing one rectangle. #include<opencv2\core\core.hpp> #include<opencv2\imgproc\imgproc.hpp> #include<opencv2\highgui\highgui.hpp> #include<stdio

问题 I am implementing a Particle Filter for a 3D cube tracking in C++. I faced a significant problem when assigning weights for the particles, because weights are based on the error in distance (in this case) between a sample point and a pixel (explained below). Currently I am able to get a video stream from the camera on my laptop, display it on a screen and draw particles on top of that. Also I am able to extract edges of objects detected from the camera and find their contours' coordinates.

问题 I'm on people tracking in computer vision. I have observations (blob as an output of blob detection after background subtraction) and I want to infer the objects that have produced these observations. I have troubled with some Kalman filter code. And it's quite clear to me, but my problem is multi-object tracking: my problem is that sometimes the observations are incomplete/noisy. Let me explain better - In a test with clear observations, I have 1 blob for each person. Kalman filter can help