A novel fast moving object contour tracking algorithm:
If a somewhat fast moving object exists in a complicated tracking environment, snake’s nodes may fall into the inaccurate local minima. We propose a mean shift snake algorithm to solve this problem. However, if the object goes beyond the limits of mean shift snake module operation in successive sequences, mean shift snake’s nodes may also fall into the local minima in their moving to the new object position. This paper presents a motion compensation strategy by using particle filter; therefore a new Particle Filter Mean Shift Snake (PFMSS) algorithm is proposed which combines particle filter with mean shift snake to fulfill the estimation of the fast moving object contour. Firstly, the fast moving object is tracked by particle filter to create a coarse position which is used to initialize the mean shift algorithm. Secondly, the whole relevant motion information is used to compensate the snake’s node positions. Finally, snake algorithm is used to extract the exact object contour and the useful information of the object is fed back. Some real world sequences are tested and the results show that the novel tracking method have a good performance with high accuracy in solving the fast moving problems in cluttered background.
A novel fast moving object contour tracking algorithm:
If a somewhat fast moving object exists in a complicated tracking environment, snake’s nodes may fall into the inaccurate local minima. We propose a mean shift snake algorithm to solve this problem. However, if the object goes beyond the limits of mean shift snake module operation in successive sequences, mean shift snake’s nodes may also fall into the local minima in their moving to the new object position. This paper presents a motion compensation strategy by using particle filter; therefore a new Particle Filter Mean Shift Snake (PFMSS) algorithm is proposed which combines particle filter with mean shift snake to fulfill the estimation of the fast moving object contour. Firstly, the fast moving object is tracked by particle filter to create a coarse position which is used to initialize the mean shift algorithm. Secondly, the whole relevant motion information is used to compensate the snake’s node positions. Finally, snake algorithm is used to extract the exact object contour and the useful information of the object is fed back. Some real world sequences are tested and the results show that the novel tracking method have a good performance with high accuracy in solving the fast moving problems in cluttered background.
thankyou Sathish, please also give references for the above algorithms.
Provided measurement and known object model, the extended Kalman filter (EKF) is most common for fast tracking. However, the noise statistics must be known in order for EKF to be near optimal. If you face problems with noise properties, use the extended unbiased FIR (EUFIR) filter (Suboptimal FIR Filtering of Nonlinear Models in Additive White Gaussian Noise, IEEE Trans. Signal Process, 60, 10, 2012). The first-order iterative EUFIR filter can be designed to be as fast as the Kalman. But, it totally ignores the noise statistics and initial errors.
Visual tracking has been a challenging problem in computer vision over the decades. The applications of visual tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy
Some Links
*www.serc.iisc.ernet.in/~venky/Papers/rvb_icassp07a.pdf
*www.bmva.org/bmvc/2002/papers/99/full_99.pdf
*research.microsoft.com/pubs/132555/fulltext.pdf
*hddv.net/archive/index.php/t-5514.html
*http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4217226&abstractAccess=no&userType=
*http://ses.library.usyd.edu.au/handle/2123/7868
*dtpapers.googlecode.com/files/report.pdf
Some Papers
*High-speed Object Tracking Based on Temporally Evaluated Optical Flow
*KERNEL-BASED SPATIAL-COLOR MODELING FOR FAST MOVING OBJECT TRACKING
You can refer to the following paper
S. Bhattacharyya, U. Maulik and P. Dutta, “High speed target tracking by fuzzy hostility induced segmentation of optical flow field”, International Journal of Applied Soft Computing, vol. 9, pp. 126-134, 2009.
Best wishes,
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