I think it really depends upon specific characteristics of the object to track, but ideally something that is invariant with changes in lighting, angle and distance. For example a laser pointer is easy to track based on brightness. One interesting note is that you should still carefully analyze the histogram for an image even with a bright object to determine its signature. Histogram analysis and thresholds derived are useful along with edge detection and shape - although shape could be an issue for tracking objects where the surface normal orientation is changing. My suggestion is to start with histogram analysis of some of your captured frames from test images using something as simple as GIMP (GNU Image Processing) or MATLAB and look at the histogram in each color band (Red, Green, Blue) - sometimes counter to intuition a red laser thresholds better in the Green band for example. The text "Computer and Machine Vision, 4th Ed.", but E.R. Davies documents well the standard machine vision methods for tracking. If research and development of new methods is more your interest, I would suggest this text - http://szeliski.org/Book/ Simple object tracking is perhaps not so simple in terms of robustness and good generalization performance. One way I like to distinguish machine vision from computer vision is that machine vision is often used in more controlled environments with the goal to automate a process, but computer vision has the goal to develop methods more like human vision that are adaptive and generalize well to work robustly in varying conditions. That's something to consider as well - which type of problem are you pursuing?
Thank you Sam !!!
I am working on Person Tracking in Video Sequences . BUt i am thinking not to track whole person instead of that i only track some portion of the person . The reason behind that is i want to deal with the Occlusion problem in tracking and when object shape is deform then this might be helpful .
Thatsfor your link .
Pleaase give suggestion on what i am thinking !!!
Ok, so have you looked into skeletal transformations and tracking the motions of the person's gate?
No ihave not looked the Skeletal Trasformations . I will read and come back to you
Chirag,
I would suggest to make use of SURF feature points, since this will help you to track the object in different orientation and scaling variations. If implementing for real time applications. Also I might be in Ahmedabad (home) by the end of Feb, if you are interested we can also discuss more.
However, of you could give more detail on the problem I might give some other ideas too. I use them for various use in tracking and obstacle avoidance for UAVs. Which holds true for any computer vision problem.
Hi,
May be you can use kalman filter. I saw this video where they use it. http://www.youtube.com/watch?v=-ahC2mn-RrQ
take look at this http://personal.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html
CamSHIFT implemented in OPENCV is a good example to do tracking. Lucas-Kanade use features based on points obtained from detectors such as Shi-Tomasi. Other detectors could be used too (Harris, SIFT, SURF, NARF). A type of Kalman Filter can be used to estimate and predict features from the sequence of images (in this way, you can only process zones in the image and to improve the speed).
If you've got the object already detected in the first frame, you can just use particle filter to track it in the subsequent frames. For making it real time, you can use feature reduction by using compressed sensing. I've recently worked on this project.. Maybe this will help.. https://www.researchgate.net/publication/261706562_Real_time_Visual_Tracking?ev=prf_pub
There are the publications mentioned in the references for further details..
Data Real time Visual Tracking
- Badges
- Science topic
- Similar topics
- Computer Communications (Networks)
- Simulators