Detection and dimension of moving objects using single camera applied to the round timber measurement

The paper is devoted to the problem of automatic geometry evaluation of the log moving through the conveyor. The video sequence obtained from the single camera is used as the input data. The principal restrictions of the target objects described for the given task, and the requirements to the video recording of the manufacturing process are formulated on the basis of datasets from more than .5M video images. The authors' method for the video sequence segmentation in respect to the log tracking is presented. The algorithm is based on the combination of background subtraction techniques and probabilistic methods. Next part of the paper is devoted to the log geometry estimation methods. The authors' algorithm for the log geometry structure recovery is based on the detection, isolation and approximation of log boundaries. The results of the research are implemented in the development of the conveyor-tracking system for automatic log sorting.

[1]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[2]  Vijay V. Vazirani,et al.  Matching is as easy as matrix inversion , 1987, STOC.

[3]  Larry S. Davis,et al.  View-based detection and analysis of periodic motion , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[4]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[5]  Karel Janák,et al.  Differences in round wood measurements using electronic 2 D and 3 D systems and standard manual method , 2007 .

[6]  Mohan M. Trivedi,et al.  Detecting Moving Shadows: Algorithms and Evaluation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[8]  Kesheng Wu,et al.  Optimizing two-pass connected-component labeling algorithms , 2009, Pattern Analysis and Applications.

[9]  Patrick Bouthemy,et al.  Motion Recognition Using Nonparametric Image Motion Models Estimated from Temporal and Multiscale Cooccurrence Statistics , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[11]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[12]  Guojun Lu,et al.  Segmentation of moving objects in image sequence: A review , 2001 .

[13]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[14]  A. V. Kruglov,et al.  Tracking of fast moving objects in real time , 2016, Pattern Recognition and Image Analysis.

[15]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  J J Koenderink,et al.  What Does the Occluding Contour Tell Us about Solid Shape? , 1984, Perception.

[17]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[18]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[19]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[20]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[21]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.