Fusion of luma and chroma GMMs for HMM-based object detection

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

Volumn 4319 LNCS, Issue , 2006, Pages 573-581

  Wang, Wen Hao ^a   Wu, Ruei Cheng ^a  

^a INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL DETECTION; OBJECT RECOGNITION;

FALSE DETECTIONS; FUSION METHODS; HMM-BASED; SEGMENTATION RESULTS; SPATIAL DOMAINS; SPATIAL TEMPORALS; TEMPORAL SEGMENTATIONS; VIDEO OBJECT DETECTIONS;

OBJECT DETECTION;

EID: 70350259884     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11949534_57     Document Type: Conference Paper

References (9)

1. Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-Time Tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, (1999) 23-25
2. Rittscher, J., Kato, J., Joga S., Blake A.: A Probabilistic Background Model for Tracking
3. Kato, J., Joga, S., Rittscher, J., Blake, A.: An HMM-Based Segmentation Method for Traffic Monitoring Movies. IEEE Trans. PAMI, Vol. 24, No. 9 (2002) 1291-1296
4. Li, N., Bu, J., Chen, C.: Real-Time Video Object Segmentation Using HSV Space. ICIP, (2002) II-85-II-88
5. Junag, B.-H., Rabiner, L.R.: Mixture Autoregressive Hidden Markov Models for Speech Signals. IEEE Trans. Acoustics, Speech, and Signal Processing, (1985) 1404-1413
6. Zivkovic, Z.: Improved Adaptive Gaussian Mixture Model for Background Subtraction. ICPR, Vol.2, (2004) 28-31
7. Dedeoglu, Y.: Human Action Recognition Using Gaussian Mixture Model Based Background Segmentation. Machine Learning Workshop, Bilkent University (2005)
8. Power, O. W.: Understanding Background Mixture Models for Foreground Segmentation. Proceedings of Image and Vision Computing New Zealand, (2002) 267-271
9. Horn, K.P.: Robot Vision, MIT Press, (1986) 299-333