Call for Papers:Vol.11 Issue.3

Submission
Last date		 28-Jun-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING
PaperId: :  26422
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 3 2025
DUI:    16.0415/IJARIIE-26422
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
M. Lakshmi kanthKV SUBBA REDDY ENGINEERING COLLEGE
V. MaheshKV SUBBA REDDY ENGINEERING COLLEGE
S. Vijay WinstonKV SUBBA REDDY ENGINEERING COLLEGE
MB. Venkata Dinesh kumar ReddyKV SUBBA REDDY ENGINEERING COLLEGE
G Emmanuel RajuKV SUBBA REDDY ENGINEERING COLLEGE

Abstract

COMPUTER SCIENCE AND ENGINEERING
Suspicious human activity recognition (SHAR), deep learning, convolutional neural network, multimedia data.
Suspicious Human activity recognition (SHAR) is crucial for improving surveillance and security systems by recognizing and reducing possible hazards in different situations. Despite the abundance of research on the subject of SHAR, current methods frequently need to be revised with restricted levels of precision and efficiency. We aim to address the problem of inaccurate and inefficient activity recognition in surveillance systems through rigorous data collection, preparation, and model training. By leveraging Convolutional Neural Networks (CNNs) and deep learning architectures, including our time-distributed CNN and Conv3D models, it achieved improved accuracy rates of 90.14% and 88.23%. The exponential growth of CCTV installations in public and private spaces has revolutionized surveillance practices but also introduced challenges in monitoring and analysis. Traditionally, security personnel manually review surveillance footage, which is labor-intensive, time-consuming, and prone to errors caused by fatigue and oversight. To address these limitations, this project proposes an automated system powered by Machine Learning (ML) and Deep Learning (DL) technologies to detect suspicious activities in real-time, significantly enhancing efficiency and accuracy. The system utilizes Convolutional Neural Networks (CNNs), a proven tool in computer vision, to analyze frames extracted from video feeds. These frames are preprocessed for noise reduction and quality optimization before feeding into the CNN model. The trained model identifies unusual patterns based on spatial and temporal dynamics, classifying activities as either “Normal” or “Suspicious.” Suspicious activities such as theft, aggression, or unauthorized movements trigger instant alerts, enabling security personnel to respond swiftly. The system also securely stores processed data for later analysis, supporting forensic investigations and improving threat prevention mechanisms. Designed for scalability, the system can operate across diverse environments, including airports, malls, corporate offices, and residential areas. By automating labor-intensive tasks, it reduces human workload while improving detection reliability and response times. The integration of multimodal analysis, combining video and audio input, further refines its accuracy. Predictive modeling techniques offer proactive threat identification, and edge computing ensures decentralized, real-time data processing directly at surveillance sites. With its advanced capabilities and adaptive design, this system positions itself as a vital tool in modern security frameworks, offering improved monitoring, reduced operational costs, and enhanced safety across large-scale installations.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE M. Lakshmi kanth, V. Mahesh, S. Vijay Winston, MB. Venkata Dinesh kumar Reddy, and G Emmanuel Raju. "SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 3 2025 Page 189-197
MLA M. Lakshmi kanth, V. Mahesh, S. Vijay Winston, MB. Venkata Dinesh kumar Reddy, and G Emmanuel Raju. "SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 11.3(2025) : 189-197.
APA M. Lakshmi kanth, V. Mahesh, S. Vijay Winston, MB. Venkata Dinesh kumar Reddy, & G Emmanuel Raju. (2025). SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING. International Journal Of Advance Research And Innovative Ideas In Education, 11(3), 189-197.
Chicago M. Lakshmi kanth, V. Mahesh, S. Vijay Winston, MB. Venkata Dinesh kumar Reddy, and G Emmanuel Raju. "SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 3 (2025) : 189-197.
Oxford M. Lakshmi kanth, V. Mahesh, S. Vijay Winston, MB. Venkata Dinesh kumar Reddy, and G Emmanuel Raju. 'SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, 2025, p. 189-197. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/MACHINE_LEARNING_FOR_REAL_TIME_HEART_DISEASE_PREDICTION_ijariie26422.pdf (Accessed : ).
Harvard M. Lakshmi kanth, V. Mahesh, S. Vijay Winston, MB. Venkata Dinesh kumar Reddy, and G Emmanuel Raju. (2025) 'SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, 11(3), pp. 189-197IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/MACHINE_LEARNING_FOR_REAL_TIME_HEART_DISEASE_PREDICTION_ijariie26422.pdf (Accessed : )
IEEE M. Lakshmi kanth, V. Mahesh, S. Vijay Winston, MB. Venkata Dinesh kumar Reddy, and G Emmanuel Raju, "SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, pp. 189-197, May-Jun 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/MACHINE_LEARNING_FOR_REAL_TIME_HEART_DISEASE_PREDICTION_ijariie26422.pdf [Accessed : ].
Turabian M. Lakshmi kanth, V. Mahesh, S. Vijay Winston, MB. Venkata Dinesh kumar Reddy, and G Emmanuel Raju. "SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 3 ().
Vancouver M. Lakshmi kanth, V. Mahesh, S. Vijay Winston, MB. Venkata Dinesh kumar Reddy, and G Emmanuel Raju. SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNING. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(3) : 189-197. Available from: https://ijariie.com/AdminUploadPdf/MACHINE_LEARNING_FOR_REAL_TIME_HEART_DISEASE_PREDICTION_ijariie26422.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Personalized Fitness Segmentation with Actionable InsightsMachine LearningAnju Tiwari Download
Driver Drowsiness Detection System Using OpenCV And IOTComputer Science And EngineeringAkshay Amrutkar Download
Stock Sense: Deep Learning based stock-market prediction toolComputer Science and Engineering (Data Science)Meet Manoj Agarwal Download
AN AI POWERED MENTAL HEATH AND WELLNESS COMPAIONComputer Science EngineeringDr . Archana B Download
PREDICTING GENETIC VARIANTS PATHOGENECITYComputer EngineeringSyed Arbeena Kausar Download
ANDROID MALWARE DETECTION USING MACHINE LEARNING TECHNIQUESCOMPUTER SCIENCE AND ENGINEERINGS. Saiful Islam Download
IMPROVING HEALTHCARE PREDICTION OF DIABETIC PATIENTS USING KNN IMPUTED FEATURES AND TRI-ENSEMBLE MODELCOMPUTER SCIENCE AND ENGINEERINGS. Mahammad Arif Download
MONEY LAUNDERING DETECTION USING MACHINE LEARNING METHODSCOMPUTER SCIENCE AND ENGINEERINGY B. Shashank Raj Download
LEVERAGING SPATIOTEMPORAL PATTERNS FOR CYBER ATTACK DETECTION IN DISTRIBUTION SYSTEMCOMPUTER SCIENCE AND ENGINEERINGV. Kumar Download
Real-Time Heart Disease Prediction Using Machine LearningCOMPUTER SCIENCE AND ENGINEERINGP. Sri Pavan Hari Charan Download
HAND AND FACE LANDMARKS DETECTION USING MEDIA PIPE AND AICOMPUTER SCIENCE AND ENGINEERINGK. V. Bharath Kumar Download
VEHICLES SPEED DETECTION SYSTEM USING OPENCVCOMPUTER SCIENCE AND ENGINEERINGS. Santosh Download
SUSPICIOUS HUMAN ACTIVITY RECOGNITION FROM SURVEILLANCE VIDEOS USING DEEP LEARNINGCOMPUTER SCIENCE AND ENGINEERINGM. Lakshmi kanth Download
DETECTION OF THYROID DISORDERS USING MACHINE LEARNING APPOARCHCOMPUTER SCIENCE AND ENGINEERINGP. Shamiulla Download
Events Recommendation SystemComputer Science and EngineeringDharsana S Download
12