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10.1002/chin.199520006

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Spatio Temporal Feature-based Weapon and Violence Detection from Video Surveillance using Deep Learning

G Anitha Siddharth Shanker M Durgam Muni Soma Sukrith S K Loakeshvaran M. Venkatraman

The increasing incidence of violence in public spaces has made developing efficient and accurate weapon detection systems necessary. A Long Short-Term Memory (LSTM) model built on the MobileNet structure has been proposed for the detection of violence and weapons. It examines each frame of a video stream to look for indications of violence or the presence of weapons. The proposed system is trained on a dataset of simulated violent incidents. The dataset includes fights, gunfights, knife attacks, and diverse environmental and lighting conditions. The system is excellent for use in surveillance and security systems and 93.86% accuracy is achieved. It can be implemented on embedded systems or mobile devices due to its high efficiency and minimal computing demands. The proposed method can increase public safety by notably increasing the efficiency of security and surveillance systems.

Public security

Feature (linguistics)

Security system

10.1109/i-smac58438.2023.10290433

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Autonomous Agents and Multi-Agent Systems (1999)

M. Venkatraman Munindar P. Singh

Interoperation

Interaction protocol

10.1023/a:1010056221226

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Citations (131)

Enhancing Accuracy in Crop Yield Estimation Through the Integration of Naive Bayes and Convolutional Neural Networks

Samanth Reddy P R Surendran M. Venkatraman

This article improves the accuracy of crop yield forecast by using convolutional neural network and Naive Bayes algorithm. Supplies and Procedures. The study comprised 42 samples, which were grouped into two sets of 21 samples each. While the second group utilised the Convolutional Neural Network technique, the first group used the Naive Bayes technique. The statistical power of the study was established at 80% with an alpha level of 0.05 and a beta level of 0.20, using G power. Results: The Naive Bayes algorithm tends to have a higher accuracy rate, which in this case is around 88%. On the other hand, the Convolutional Neural Network algorithm has a slightly lower accuracy rate of approximately 82%. Independent sample t-test using this paper. It has a significant value of p is .046 (p<0.05), this is statistically significant. Conclusion: The Naive Bayes algorithm was discovered to exhibit a higher level of accuracy in comparison to the Convolutional Neural Network Algorithm.

10.1109/iccebs58601.2023.10449096

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