Unofficial page of the course Dynamic Scene Analysis

About the course

The course considers computer vision subfields which deal with moving objects and moving cameras. The official pages are here.

Lecturers:

Required reading (choose at least one subject)

Background modelling

• C. Stauffer; W. Grimson. Adaptive background mixture models for real-time tracking. CVPR 1999. pp. 246-252. pdf.
• Massimo Piccardi. Background subtraction techniques: a review. CSMC 2004. pp. 3099–3104. pdf.
• Sebastian Brutzer, Benjamin Hoferlin and Gunther Heidemann. Evaluation of background subtraction techniques for video surveillance CVPR 2011. pp. 1937–1944. pdf.

Feature extraction

• C. Harris and M. Stephens. A combined corner and edge detector. Alvey Vision Conference. pp. 147-151. 1988. pdf
• D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision. 60(2):91-110. 2004. pdf

Feature tracking and optical flow

• B.D. Lucas and T. Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision. International Joint Conference on Artificial Intelligence. pp. 674-679. Vancouver, Canada, 1981. CiteSeer
• J. Shi and C. Tomasi. Good Features to Track. IEEE Conference on Computer Vision and Pattern Recognition. 1994. pdf
• B.K.P. Horn and B.G. Schunck. Determining optical flow. Artificial Intelligence. vol 17. pp 185-203. 1981. CiteSeer
• Deqing Sun, Stefan Roth, and Michael J. Black. A quantitative analysis of current practices in optical flow estimation and the principles behind them. IJCV 106(2) 2014. pp. 115–137. pdf.

Object tracking

• Dorin Comaniciu, Visvanathan Ramesh and Peter Meer. Kernel-based object tracking. PAMI 25 2003. pp. 564–577. pdf.
• Gary R. Bradski. Real time face and object tracking as a component of a perceptual user interface. WACV 1998. pp. 214–219. pdf.
• Zdenek Kalal, Krystian Mikolajczyk and Jiri Matas. Tracking-learning-detection. PAMI 34(7) 2012. pp. 1409–1422. pdf.

Estimation and filtering

• S. Choi, T. Kim, W. Yu. Performance Evaluation of RANSAC Family. British Machine Vision Conference. 2009. pdf
• M. Isard and A. Blake. CONDENSATION -- conditional density propagation for visual tracking. International Journal of Computer Vision. 29(1):5-28. 1998. home
• Greg Welch and Gary Bishop. An Introduction to the Kalman Filter. TR 95-041 2006. pdf.

Camera calibration, multiple view geometry

• Z. Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(11):1330-1334. 2000. home
• H. C. Longuet-Higgins. A computer algorithm for reconstructing a scene from two projections. Nature 293(5828):133-135. 1981. pdf
• David Nistér. An Efficient Solution to the Five-Point Relative Pose Problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6):756-777 (2004) pdf
• Richard I. Hartley. In Defense of the Eight-Point Algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 19(6): 580-593 (1997) pdf

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