Analysis of Real-Time Video for the Detection of Fire Using OpenCV

  • M. Gandhi M. Gandhi Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.
  • S. Manikandan S. Manikandan Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
  • B. Vaidianathan B. Vaidianathan Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
Keywords: Fire Detection from Real-Time, Video Using Opencv, Data Augmentation Techniques, Adversarial Networks, Network Algorithm


Because of the wide range of colours and textures present in visual landscapes, fire detection is a challenging undertaking. To get over this issue, several fire image categorization methods have been suggested; nevertheless, the majority of these systems depend on rule-based methods or characteristics that are manually created. Develop and propose an innovative technique for fire picture detection using deep convolution neural networks. Adaptive piece-wise linear units are utilised in the network's hidden layers in place of conventional rectified linear units or tangent functions. In addition, we will generate a fresh, compact dataset of fire photos to use for model training and evaluation. Increasing the amount of training images available through the use of conventional data augmentation methods and generative adversarial networks helps alleviate the overfitting issue that arises from training the network on a small dataset. In this study, we compare and contrast two methods for measuring the geometrical features of wildland fires: one that uses image processing to identify colours, and the other that uses Mk2ethods. Presented here are two novel rules and two novel detection methods that make use of an intelligent combination of the rules; their respective performances are then evaluated. About 270 million non-fire pixels and 200 million fire pixels taken from 500 wild terrain photos taken under different imaging conditions are used to run the benchmark. Color and presence of fire are used to classify pixels as fire, whereas average intensity of the associated image is used to classify pixels as non-fire. Because of this, the future of Metrologic systems for detecting fires in unstructured environments looks bright thanks to this technology.


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121. Chakrabarti P., De S.K., Sikdar S.C., “Statistical Quantification of Gain Analysis in Strategic Management” , International Journal of Computer Science and Network Security,9(11), pp.315-318, 2009.

122. Chakrabarti P. , Basu J.K. , Kim T.H., “Business Planning in the light of Neuro-fuzzy and Predictive Forecasting”, Communications in Computer and Information Science , 123, pp.283-290, 2010.

123. Prasad A. , Chakrabarti P., “Extending Access Management to maintain audit logs in cloud computing", International Journal of Advanced Computer Science and Applications ,5(3),pp.144-147, 2014.

124. Sharma A.K., Panwar A., Chakrabarti P. ,Viswakarma S., “Categorization of ICMR Using Feature Extraction Strategy and MIR with Ensemble Learning”, Procedia Computer Science, 57,pp.686-694,2015.

125. Patidar H. , Chakrabarti P., “A Novel Edge Cover based Graph Coloring Algorithm”, International Journal of Advanced Computer Science and Applications , 8(5),pp.279-286,2017.

126. Patidar H., Chakrabarti P., Ghosh A., “Parallel Computing Aspects in Improved Edge Cover based Graph Coloring Algorithm”, Indian Journal of Science and Technology ,10(25),pp.1-9,2017.

127. Tiwari M., Chakrabarti P, Chakrabarti T., “Novel work of diagnosis in liver cancer using Tree classifier on liver cancer dataset ( BUPA liver disorder )” , Communications in Computer and Information Science , 837, pp.155-160, 2018.

128. Verma K., Srivastava P. , Chakrabarti P., “Exploring structure oriented feature tag weighting algorithm for web documents identification”, Communications in Computer and Information Science ,837, pp.169-180, 2018.

129. Tiwari M., Chakrabarti P , Chakrabarti T., “Performance analysis and error evaluation towards the liver cancer diagnosis using lazy classifiers for ILPD”, Communications in Computer and Information Science , 837, pp.161-168,2018.

130. J. Angelin Jeba, S. Rubin Bose, R. Regin, M.B. Sudhan, S. Suman Rajest and P. Ramesh Babu “Efficient Real-time Tamil Character Recognition via Deep Vision Architecture,” AVE Trends In Intelligent Computing Systems, vol. 1, no. 1, pp. 1 –16, 2024.

131. K. D. Jasper, M.N. Jaishnav, M. F. Chowdhury, R. Badhan and R. Sivakani “Defend and Secure: A Strategic and Implementation Framework for Robust Data Breach Prevention,” AVE Trends In Intelligent Computing Systems, vol. 1, no. 1, pp. 17 –31, 2024.

132. P. P. Anand, G. Jayanth, K. S. Rao, P. Deepika, M. Faisal, and M. Mokdad “Utilising Hybrid Machine Learning to Identify Anomalous Multivariate Time-Series in Geotechnical Engineering,” AVE Trends In Intelligent Computing Systems, vol. 1, no. 1, pp. 32-41, 2024.

133. M. Usman and A. Ullah, “Blockchain Technology Implementation in Libraries: An Overview of Potential Benefits and Challenges,” AVE Trends In Intelligent Computing Systems, vol. 1, no. 1, pp. 42 –53, 2024.

134. P. Jani, D. Nanban, J. Selvan, N. Richardson, R. Sivakani, and R. Subhashni, “Studying Price Dynamics of Bus Services Using Machine Learning Algorithms,” AVE Trends In Intelligent Computing Systems, vol. 1, no. 1, pp. 54 –65, 2024.

135. P. S. Venkateswaran, S. D. Dharshini, S. K. Kumar, D. Lakshmi, D. Balan, and D. K. Sachani, “A Study on Market Share Analysis of Select Food Products: Identifying Key Drivers and Barriers to Growth,” AVE Trends In Intelligent Social Letters, vol. 1, no. 1, pp. 1–12, 2024.

136. Patidar H. , Chakrabarti P., “A Tree-based Graphs Coloring Algorithm Using Independent Set”, Advances in Intelligent Systems and Computing, 714, pp. 537-546, 2019.

137. Chakrabarti P., Satpathy B., Bane S., Chakrabarti T., Chaudhuri N.S. , Siano P., “Business forecasting in the light of statistical approaches and machine learning classifiers”, Communications in Computer and Information Science , 1045, pp.13-21, 2019.

138. Shah K., Laxkar P. , Chakrabarti P., “A hypothesis on ideal Artificial Intelligence and associated wrong implications”, Advances in Intelligent Systems and Computing, 989, pp.283-294, 2020.

139. Kothi N., Laxkar P. Jain A. , Chakrabarti P., “Ledger based sorting algorithm”, Advances in Intelligent Systems and Computing, 989, pp. 37-46, 2020.

140. Chakrabarti P. ,Chakrabarti T., Sharma M . , Atre D, Pai K.B., “Quantification of Thought Analysis of Alcohol-addicted persons and memory loss of patients suffering from stage-4 liver cancer”, Advances in Intelligent Systems and Computing, 1053, pp.1099-1105, 2020.

141. Chakrabarti P., Bane S.,Satpathy B.,Goh M, Datta B N , Chakrabarti T., “Compound Poisson Process and its Applications in Business”, Lecture Notes in Electrical Engineering, 601, pp.678-685,2020.

142. Rafi, H., & Farhan, M. (2016). Dapoxetine: An Innovative Approach in Therapeutic Management in Animal Model of Depression. Pak. J. Pharm. Res, 2(1), 15-22.

143. Bhuva, D., & Kumar, S. (2023). Securing space cognitive communication with blockchain. 2023 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW). IEEE.

144. Bhuva, D. R., & Kumar, S. (2023). A novel continuous authentication method using biometrics for IOT devices. Internet of Things, 24(100927)
How to Cite
M. Gandhi, M. G., S. Manikandan, S. M., & B. Vaidianathan, B. V. (2024). Analysis of Real-Time Video for the Detection of Fire Using OpenCV. International Journal of Human Computing Studies, 6(2), 36-56.