Adaptive Scale Mean-Shift Tracking with Gradient Histogram
The mean-shift (MS) tracking is fast, is easy to implement, and performs well in many conditions especially for object with rotation and deformation. But the existing MS-like algorithms always have inferior performance for two reasons: the loss of pixel’s neighborhood information and lack of template update and scale estimation. We present a new adaptive scale MS algorithm with gradient histogram to settle those problems. The gradient histogram is constructed by gradient features concatenated with color features which are quantized into the 16 × 16 × 16 × 16 bins. To deal with scale change, a scale robust algorithm is adopted which is called background ratio weighting (BRW) algorithm. In order to cope with appearance variation, when the Bhattacharyya coefficient is greater than a threshold the object template is updated and the threshold is set to avoid incorrect updates. The proposed tracker is compared with lots of tracking algorithms, and the experimental results show its effectiveness in both distance precision and overlap precision.
KeywordsObject tracking Mean-shift Scale estimation Gradient
This work was supported by the National Natural Science Foundation of China (Grant No. 61501139) and the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology (HIT.NSRIF.2013136).
- 2.Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift. In: Proceedings IEEE conference on computer vision and pattern recognition, 2000. IEEE; 2000, vol. 2. p. 142–9.Google Scholar
- 4.Collins RT. Mean-shift blob tracking through scale space. In: Proceedings IEEE computer society conference on computer vision and pattern recognition, 2003. IEEE; 2003, vol. 2. p. II-234.Google Scholar
- 6.Canny J. A computational approach to edge detection. In: Readings in computer vision; 1987. p. 184–203.Google Scholar
- 7.Sobel I. An isotropic 3 × 3 image gradient operator. In: Machine vision for three-dimensional scenes; 1990. p. 376–9.Google Scholar
- 8.Wu Y, Lim J, Yang MH. Online object tracking: A benchmark. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE; 2013. p. 2411–8.Google Scholar
- 9.Grabner H, Grabner M, Bischof H. Real-time tracking via on-line boosting. Bmvc; 2006, vol. 1, no. 5. p. 6.Google Scholar