REAL-TIME SMOKE DETECTION IN VIDEO
Article Sidebar
Main Article Content
Abstract
The paper considers algorithmic and software for early fire detection based on smoke detection from video sequences generated by a static video camera. To detect areas with smoke, an algorithm has been developed that allows you to select such areas on video frames that are characterized by a number of features: the presence of a stable directional movement, compliance with the color characteristics of smoke, and a decrease in the energy value of highfrequency components relative to the background model. The feature of the algorithm is a step-by-step spatiotemporal analysis of candidate areas, which provides satisfactory computational costs and real-time operation on modern computing tools for high-resolution video frames. The algorithm is implemented using the functions of the OpenCV computer vision library and multi-threaded processing. The features and main functionality of the software implemented as a stationary application are given. The results of experimental studies on the evaluation of the efficiency of the algorithm and its speed are presented.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Cetin, A., Merci, B., Gunay, O., & Toreyin, B. U. (2016). Methods and Techniques for Fire Detection: Signal, Image and Video Processing Perspectives. Elsevier.
Pyataeva, A. V. (2017). Issledovanie metodov i razrabotka algoritmov obnaruzheniya dyma na otkrytykh prostranstvakh po videoposledovatel'nostyam: monografiya [Investigation of methods and development of smoke detection algorithms in open spaces from video sequences: monograph]. Krasnoyarsk: SibFU. (In Russ.).
Hashemzadeh, M., Farajzadeh, N., & Heydari, M. (2022). Smoke detection in video using convolutional neural networks and efficient spatio-temporal features. Applied Soft Computing, (128). DOI: 10.1016/j.asoc.2022.109496.
Bohush, R., Brovko, N., & Ablameyko, S. (2013). Fire Detection in Video Sequences Based on Static and Dynamic Features. Journal of Electrical Engineering, 1(1), 25–33. DOI: 10.17265/2328-2223/2013.12.004.
Bogush, R. P., Tychko, D. A. (2015). Algoritm kompleksnogo obnaruzheniya dyma i plameni na osnove analiza dannykh sistem videonablyudeniya [Comprehensive smoke and flame detection algorithm based on video surveillance data analysis]. Doklady BGUIR, 6(92), 65–71 (In Russ.)
Zivkovic, Z. (2004). Improved adaptive gaussian mixture model for background subtraction. In J. Kittler, M. Petrou, & M. Nixon (Eds.), Proceedings of the 17th International Conference on Pattern Recognition. ICPR 2004: vol. 2 (28–31). DOI: 10.1109/ICPR.2004.1333992.
Ye, S., Zhican, B., Chen, C., Bohush, R., & Ablameyko, S. (2017). An Effective Algorithm to Detect Both Smoke and Flame Using Color and Wavelet Analysis. Pattern Recognition and Image Analysis, 27(1), 131–138. DOI: 10.1134/S1054661817010138.
Farneback, G. (2003). Two-Frame Motion Estimation Based on Polynomial Expansion. In J. Bigun, T. Gustavsson (Eds.), Image Analysis. SCIA 2003. Lecture Notes in Computer Science: vol. 2749 (363–370). Berlin, Heidelberg: Springer. DOI: 10.1007/3-540-45103-X_50.
Most read articles by the same author(s)
- D. GLUKHOV, R. BOHUSH, T. GLUKHOVA, SPARE MATRIX REPRESENTATION USING ASSOCIATIVE CONTAINERS C ++ STL LIBRARIES, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 12 (2020)
- A. HODOSEVICH, R. BOHUSH, CLIENT-SERVER SYSTEM FOR PARKING MANAGEMENT BASED ON VIDEO SURVEILLANCE DATA ANALYSIS, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 4 (2022)
- R. BOHUSH, Y. ADAMOVSKIY, V. CHERTKOV, DATA COMPOSITION AND REPRESENTATION FOR COGNITIVE COMMUNICATION SYSTEM MODEL BASED ON LTE, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 12 (2021)
- R. BOHUSH, S. ABLAMEYKO, I. ZAHKARAVA, CALCULATION AND ANALYSIS OF MOVING OBJECT FEATURES FOR TRACKING IN VIDEO SEQUENCES, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 4 (2021)
- R. BOHUSH, I. ZAKHARAVA, Y. PASTUKHOV, D. PASTUKHOV, N. NAUMOVICH, SIMULATION OF EARTH REMOTE SENSING DATA COMPRESSION BASED ON BLOCK ADAPTIVE QUANTIZATION, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 4 (2019)
- R. BOHUSH, I. ZAHAROVA, V. CHERTKOV, N. NAUMOVICH, SPACECRAFT SAR DATA PROCESSING ALGORITHMS MODELING FOR TRANSMITTING AND RADAR IMAGE FORMATION, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 12 (2018)
- R. BOHUSH, I. ZAKHARAVA, CLIENT-SERVER SYSTEM FOR PEOPLE IDENTIFICATION AND TRACKING IN INDOOR BASED ON VIDEO DATA ANALYSIS, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 12 (2020)
- Y. ADAMOVSKIY, R. BOHUSH, V. CHERTKOV, N. NAUMOVICH, I. STEGKO, USER ACTIVITY MODELING BASED ON MARKOV CHAIN FOR RADIO ENVIRONMENT MAP IN COGNITIVE RADIO NETWORKS, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 11 (2022)
- R. BOHUSH, I. ZAKHARAVA, N. NAUMOVISH, SIMULATION OF COMPRESSION ECBAQ MODIFICATION WITH REPRESENTATION IN CEOS FORMAT OF EARTH REMOTE SENSING DAT, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 12 (2017)
- V. CHERTKOV, R. BOHUSH, N. NAUMOVICH, FEATURES STRUCTURE AND FORMAT OF PRESENTATION OF RADAR SATELLITE DATA TERRASAR-X, Vestnik of Polotsk State University. Part C. Fundamental Sciences: No. 4 (2017)