Complicated Shapes Estimation Method for Objects Analysis in Video Surveillance Systems
DOI:
https://doi.org/10.20535/1810-0546.2018.3.136433Keywords:
DEI approach, Image feature extraction, Vector filtering of image, Vector contour analysisAbstract
Background. The evaluation of video image objects is a relatively difficult task. While solving the task of the geometric representation of a surveillance object, the following additional factors should be considered: possible overlapping of objects, similarity of complex elements, similarity of object elements and background, etc.
Objective. The development of a method for complicated objects shape evaluation for application in video surveillance systems for estimation of dynamics of an object’s movement, examination of the object’s behavior on a probable execution of unauthorized actions, and for other tasks.
Methods. The procedure of the background subtraction is used for identification of a raster shape of the surveillance object. To detect a vector shape of the object contours, the DEI approach is applied. The sorting procedures are used for identification of reference contour points and for forming the smooth curves.
Results. The proposed method includes the following stages: color space conversion and normalization, object shape detection, contours detection and analysis, sorting of vector data, forming of smooth contour curve, object area computing. When the contour points number is reduced in 1.5 times, an average error of the proposed method compared with the DEI approach for accuracy rate is 0.75 %, for performance rate it is 8.43 %, for resource consuming rate it is 3.09 %.
Conclusions. The proposed method allows to define an array of vector contour points which represent an “approximate” surveillance object of a complicated shape and it minimizes the data volume to be used in further analysis of a motion trajectory and other similar tasks without decreasing the accuracy. In addition, this method enables describing the surveillance object by an equal quantity of contour points that in turn can simplify the task of surveillance objects classification.References
D.J. Dailey and L. Li, “Video image processing to create a speed sensor”, University of Washington, Res. Rep. 52, 2000.
A.N. Rajagopalan and R. Chellappa, “Vehicle detection and tracking in video”, in IEEE Proc. Image Proc., Vancouver, BC, Canada, 2000, pp. 351–354. doi: 10.1109/ICIP.2000.900967
R. Cucchiara et al., “Image analysis and rule-based reasoning for a traffic monitoring system”, IEEE Tran. Intell. Transport. Syst., vol. 1, no. 2, pp. 119–130, 2000. doi: 10.1109/6979.880969
B.L. Tseng et al., “Real-time video surveillance for traffic monitoring using virtual line analysis”, in IEEE Proc. Multimedia and Expo, ICME 2002, Lausanne, Switzerland, 2002, vol. 2, pp. 541–544. doi: 10.1109/ICME.2002.1035671
V. Kastrinaki et al., “A survey of video processing techniques for traffic applications”, Image and Vision Computing, vol. 21, no. 4, pp. 359–381, 2003. doi: 10.1016/S0262-8856(03)00004-0
G. Marbach et al., “An image processing technique for fire detection in video images”, Fire Safety J., vol. 41, no. 4, pp. 285–289, 2006. doi: 10.1016/j.firesaf.2006.02.001
S.E. Memane and V.S. Kulkarni, “A review on flame and smoke detection techniques in video’s”, Int. J. Adv. Res. Electrical, Electronics Instrumen. Eng., vol. 4, no. (2), pp. 885–889, 2015.
E. Cabello et al., “A new approach to identify big rocks with applications to the mining industry”, Real Time Imaging, vol. 8, no. 1, pp. 1–9, 2002. doi: 10.1006/rtim.2000.0255
R.J. Ferraria et al., “Real-time detection of steam in video images”, Pattern Recogn., vol. 40, no. 3, pp. 1148–1159, 2007. doi: 10.1016/j.patcog.2006.07.007
B. Munzer et al., “Content-based processing and analysis of endoscopic images and videos: A survey”, Multimedia Tools and Applications, vol. 77, no. 1, pp. 1323–1362, 2018. doi: 10.1007/s11042-016-4219-z
H.G. Moghaddam and W.H. Enright, “A scattered data interpolant for the solution of three dimensional PDEs”, in Proc. Eur. Conf. Computational Fluid Dynamics, ECCOMAS CFD 2006, Netherland, 2006.
H.G. Moghaddam and W.H. Enright, “Efficient contouring on unstructured meshes for partial differential equations”, ACM Trans. Math. Software, vol. 34, no. 4, 2007. doi: 10.1145/1377596.1377599
W.H. Enright, “Accurate approximate solution of partial differential equations at off-mesh points”, ACM Trans. Math. Software, vol. 26, no. 2, pp. 274–292, 2000. doi: 10.1145/353474.353482
G. Barequet and M. Sharir, “Piecewise-linear interpolation between polygonal slices”, Computer Vision and Image Understanding, vol. 63, no. 2, pp. 251–272, 1996. doi: 10.1145/177424.177562
S. Lee et al., “Scattered Data Interpolation with Multilevel B-Splines,” IEEE Trans. Visual. Comp. Graphics, vol. 3, no. 3, pp. 228–244, 1997. doi: 10.1109/2945.620490
Matlab Documentation [Online]. Available: https://www.mathworks.com/help/matlab
R.C. Gonzalez and R.E. Woods, Digital Image Processing, 2nd ed. Prentice Hall, NJ, 2002.
I. Pitas and A.N. Venetsanopoulos, Nonlinear Digital Filters: Principles and Applications. Boston, MA: Kluwer, 1990, 392 p.
V.V. Khryashchev et al., “Image denoising using adaptive switching median filter”, IEEE Int. Conf. Image Process., vol. 1, pp. 117–120, 2005. doi: 10.1109/ICIP.2005.1529701
Y. Zhao et al., “Performance enhancement and analysis of an adaptive median filter”, in Proc. Int. Conf. Commun. Networking, 2007, pp. 651–653. doi: 10.1109/CHINACOM.2007.4469475
S. Shrestha, “Image denoising using new adaptive based median filter”, Signal & Image Processing: An Int. J., vol. 5, no. 4, pp. 1–13, 2014. doi: 10.5121/sipij.2014.5401
P. Su and R.L.S. Drysdale, “A comparison of sequential Delaunay triangulation algorithms”, Computational Geometry, vol. 7, no. 5-6, pp. 61–70. 1995. doi: 10.1016/S0925-7721(96)00025-9
Downloads
Published
Issue
Section
License
Copyright (c) 2018 Igor Sikorsky Kyiv Polytechnic Institute
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under CC BY 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
