Mean shift parallel tracking on GPU

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5524 LNCS, Issue , 2009, Pages 120-127

  Li, Peihua ^a   Xiao, Lijuan ^a  

^a HEILONGJIANG UNIVERSITY   (China)

Author keywords

CUDA; GPU; Mean Shift tracking; Parallel algorithm

Indexed keywords

COLOR DISTRIBUTION; COLOR HISTOGRAM; COMPUTE UNIFIED DEVICE ARCHITECTURES; CUDA; GPU; GRAPHICS PROCESSING UNIT; K-MEANS CLUSTERING; KERNEL FUNCTION; KEY COMPONENT; MEAN SHIFT; MEAN SHIFT TRACKING; MEAN SHIFT VECTOR; OBJECT COLORS; PARALLEL COMPUTATION; PARALLEL IMPLEMENTATIONS; TRACKING ALGORITHM; TRACKING PERFORMANCE;

COLOR; COLOR IMAGE PROCESSING; IMAGE ANALYSIS; PATTERN RECOGNITION; VECTOR QUANTIZATION;

PARALLEL ALGORITHMS;

EID: 68849103535     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02172-5_17     Document Type: Conference Paper

References (11)

1. Comaniciu, D., Ramesh, V., Meer, P.: Real-time Tracking of Non-rigid Objects Using Mean Shift. In: Proc. IEEE Conf. Comp. Vis. Patt. Recog., pp. 142-149. Hilton Head Island, South Carolina (2000)
2. Collins, R.T.: Mean-shift Blob Tracking Through Scale Space. In: Proc. IEEE Conf. Comp. Vis. Patt. Recog., Madison, Wisconsin, pp. 234-241 (2003)
3. Birchfield, S.T., Rangarajan, S.: Spatiograms versus Histograms for Region-based Tracking. In: Proc. IEEE Conf. Comp. Vis. Patt. Recog., San Diego, CA, USA, pp. 1158-1163 (2005)
4. Zhao, Q., Tao, H.: Object Tracking using Color Correlogram. In: IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) in conjunction with ICCV, pp. 263-270 (2005)
5. Yilmaz, A.: Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection. In: Proc. IEEE Conf. Comp. Vis. Patt. Recog., Minneapolis, Minnesota, pp. 1-6 (2007)
6. Zhang, H., Huang, W., Huang, Z., Li, L.: Affine object tracking with kernel-based spatial-color representation. In: Proc. IEEE Conf. Comp. Vis. Patt. Recog., San Diego, CA, USA, pp. 293-300 (2005)
7. Leichter, I., Lindenbaum, M., Rivlin, E.: Visual Tracking by Affine Kernel Fitting Using Color and Object Boundary. In: Proc. Int. Conf. Comp. Vis., Rio de, Janeiro, Brazil, pp. 1-6 (2007)
8. NVIDIA CUDA Homepage, http://developer.nvidia.com/object/cuda.html
9. Li, P.: A clustering-based color model and integral images for fast object tracking. Signal Processing: Image Communication, 676-687 (2006)
10. EC funded CAVIAR project/IST 2001 37540 (2004), http://homepages.inf.ed. ac.uk/rbf/CAVIAR/
11. Bajramovic, F., Grabl, C., Denzler, J.: Efficient Combination of Histograms for Real-Time Tracking Using Mean-Shift and Trust-Region Optimization. In: Proc. 27th DAGM Symposium on Pattern Recognition, Vienna, Austria, pp. 254-261 (2005)