Moving Object Detection on CCTV Surveillance Using the Frame Difference Method
Abstract
In today's computer vision research, many build systems for observing humans and understanding their appearance, activities, and behaviour that provide sophisticated interfaces for interacting with humans, and create plausible human models for various purposes. This paper presents a simple algorithm for detecting moving objects from a static background based on frame differences. First, the first frame is captured via a static camera such as Closed Circuit Television (CCTV) after which a sequence of frames is taken periodically. Second, the absolute difference is calculated between successive frames and the difference in images is stored in the system. Third, the difference image is converted into a grey image and then translated into a binary image. Finally, morphological filtering is carried out to remove noise. In the last process, moving objects can be detected in conditions that do not change much apart from moving objects.
Downloads
References
Z. Zhang et al., “Deep learning based human action recognition: A survey,†in 2017 Chinese Automation Congress (CAC), 2017, no. 2014ggx10 3038, hal. 3780–3785, doi: 10.1109/CAC.2017.8243438.
Saluky Saluky; Suhono Harso Supangkat; Fetty Fitriyanti Lubis, “Moving Image Interpretation Models to Support City Analysis,†2018 Int. Conf. ICT Smart Soc., 2018.
H. Wei, “A block-wise frame difference method for real-time video motion detection,†no. August, hal. 1–13, 2018, doi: 10.1177/1729881418783633.
C. S. Rao dan P. Darwin, “Frame Difference And Kalman Filter Techniques For Detection Of Moving Vehicles In Video Surveillance,†vol. 2, no. 6, hal. 1168–1170, 2012.
Z. Wang, Y. Zhao, J. Zhang, dan Y. Guo, “Research on motion detection of video surveillance system,†Proc. - 2010 3rd Int. Congr. Image Signal Process. CISP 2010, vol. 1, hal. 193–197, 2010, doi: 10.1109/CISP.2010.5647987.
R. S. Rakibe dan B. D. Patil, “Background Subtraction Algorithm Based Human Motion Detection,†Int. J. Sci. Res. Publ., vol. 3, no. 5, hal. 3–6, 2013.
K.Kavitha;A.Tejaswini, “VIBE: Background Detection and Subtraction for Image Sequences in Video,†Int. J. Comput. Sci. Inf. Technol., vol. 3, no. 5, hal. 5223–5226, 2012, doi: 10.1111/den.13249.
A. A. Shafie, F. Hafiz, dan M. H. Ali, “Motion detection techniques using optical flow,†World Acad. Sci. Eng. Technol., vol. 56, no. 8, hal. 559–561, 2009, doi: 10.5281/zenodo.1071465.
S. Wei, Z. Chen, dan H. Dong, “Motion detection based on temporal difference method and optical flow field,†2nd Int. Symp. Electron. Commer. Secur. ISECS 2009, vol. 2, no. May, hal. 85–88, 2009, doi: 10.1109/ISECS.2009.62.
K. S. Devi, N. Malmurugan, dan M. Manikandan, “Object motion detection in video frames using background frame matching,†Int. J. Comput. Trends Technol., vol. 4, no. 6, hal. 1928–1931, 2013.
N. Lu, J. Wang, Q. H. Wu, dan L. Yang, “An Improved Motion Detection Method for Real-Time Surveillance,†IAENG Int. J. Comput. Sci., vol. 35, no. 1, hal. 1–10, 2008.
L. Shang, F. You, C. Han, X. Wang, dan S. Zhao, “Optimization of Three - Frame Difference Method and Improvement of Pedestrian Detection Code Book Optimization of Three - Frame Difference Method and Improvement of Pedestrian Detection Code Book,†2019, doi: 10.1088/1742-6596/1302/2/022014.
N. Singla, “Motion Detection Based on Frame Difference Method,†vol. 4, no. 15, hal. 1559–1565, 2014.
