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Archive Issue – Vol.4, Issue.2 (Apr-Jun 2024)

Archive Issue – Vol.4, Issue.2 (April-June 2024)


A Review on Accident Prevention Methods at Railway Line Crossings

Shobhit Gakkhar, Bhupendra Panchal

Research Paper | Journal Paper

Vol.4, Issue.2, pp.1-05, Jun-2024

Abstract References Citation Download

Abstract

Numerous systems have been developed to track trains at unmanned railway crossings and detect obstacles while automatically operating gates. Existing systems efficiently open and close gates to prevent accidents during train arrivals and departures. However, accidents still occur, often due to pedestrians distracted by headphones or mobile devices. A review of existing literature indicates that no system currently addresses this issue. The objective of this paper is to review various real-time accident prevention methods and highlight their limitations. To overcome these challenges, a system is required that can detect trains in real-time—recognizing train horns even in noisy environments—and alert or pause audio/video playback for individuals at risk. Such a system could significantly reduce accidents at railway crossings.

Key-Words / Index Term: Railway Crossing, Unmanned, Speech Recognition, Hidden Markov Model, Accident Prevention, Sensors.

References

      1. Nisha S. Punekar, Archana A. Raut, “Improving Railway Safety with Obstacle Detection and Tracking System using GPS-GSM Model”, International Journal of Scientific & Engineering Research, Volume 4, Issue 8, August 2013.
      2. Sina Aminmansour, et al., “Near-Miss Event Detection at Railway Level Crossings”, IEEE, 2014.
      3. K. Vidyasagar et al., “Anti Collision and Secured Level Crossing System”, International Journal of Computer Applications (0975–8887), Volume 107 – No. 3, December 2014.
      4. Marco Govoni, Francesco Guidi et al., “Ultra-Wide Bandwidth Systems for the Surveillance of Railway Crossing Areas”, IEEE, 2015.
      5. Bhuvaneswari P.T.V. et al., “Wheel Detection based Level Crossing Safety System”, International Conference on Computational Intelligence and Communication Networks, IEEE, 2015.
      6. B. Brailson Mansingh et al., “Automation in Unmanned Railway Level Crossing”, IEEE, 2015.
      7. Eric Trudel et al., “Data-Driven Modeling Method for Analyzing Grade Crossing Safety”, IEEE, 2016.
      8. Dr. Velayutham R. et al., “Controlling Railway Gates Using Smart Phones by Tracking Trains with GPS”, International Conference on Circuits, Power and Computing Technologies, IEEE, 2017.
      9. Pranjali Gajbhiye et al., “VIRTUe: Video Surveillance for Rail-Road Traffic Safety at Unmanned Level Crossings”, IEEE, 2017.
      10. Automated and Unmanned Control System of Railway Crossing – Seminarsonly

Citation

Shobhit Gakkhar, Bhupendra Panchal "A Review on Accident Prevention Methods at Railway Line Crossings" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.2, pp.1-05, 2024.

Unusual Crowd Activity Detection Using Opencv and Motion Influence Map For Prior Appraisal Against Crime

Swati Bhargava, Arun Jhapate

Research Paper | Journal Paper

Vol.4, Issue.2, pp.06-11, Jun-2024

Abstract References Citation Download

Abstract

Suspicious behavior is dangerous in public areas that may cause heavy causalities. There are various systems developed on the basis of video frame acquisition where motion or pedestrian detection occur but those systems are not intelligent enough to identify the unusual activities even at real time. It is required to recognized scamper situation at real time from video surveillance for quick and immediate management before any casualties. Proposed system focuses on recognizing suspicious activities and target to achieve a technique which is able to detect suspicious activity automatically using computer vision. Here system uses OpenCV library for classifying different kind of actions at real time. The motion influence map has been used to represent the motion analysis that frequently changes the position from one place to another. System uses pixel level presentation for making it easy to understand or identify the actual situation.

Key-Words / Index Term: Unusual Activity Detection, Action Recognition, Motion Influence Map, OpenCV, Crowd based Activity Detection.

References

      References
      1. D. Lee, H. Suk, S. Park and S. Lee, "Motion Influence Map for Unusual Human Activity Detection and Localization in Crowded Scenes," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 10, pp. 1612-1623, Oct. 2015.
      2. Donghui Song, "YouTube Video,” Published on Jun 13, 2018.
      3. D. Lee, H. Suk, S. Park and S. Lee, "Motion Influence Map for Unusual Human Activity Detection and Localization in Crowded Scenes," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 10, pp. 1612-1623, Oct. 2015.
      4. Zakia Hammal, Wen-Sheng Chu, Jeffrey F. Cohn, Carrie Heike, and Matthew L. Speltz, “Automatic Action Unit Detection in Infants Using Convolutional Neural Network”, 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), IEEE.
      5. He Xu, Chengcheng Yuan, Peng Li, Yizhuo Wang, “Design and Implementation of Action Recognition System Based on RFID Sensor”, 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE.
      6. Mrs. Varsha Shrirang Nanaware, Dr. Mohan Harihar Nerkar and Dr. C.M. Patil, “A Review of the Detection Methodologies of Multiple Human Tracking & Action Recognition in a Real Time Video Surveillance”, IEEE ICPCSI 2017.
      7. Jiahao Li, Hejun Wu and Xinrui Zhou, “PeMapNet: Action Recognition from Depth Videos Using Pyramid Energy Maps on Neural Networks”, 2017 International Conference on Tools with Artificial Intelligence, IEEE.
      8. Nour El Din Elmadany, Yifeng He and Ling Guan, “Information Fusion for Human Action Recognition via Biset/Multiset Globality Locality Preserving Canonical Correlation Analysis”, IEEE Transactions on Image Processing, 2018.
      9. Soumalya Sen, Moloy Dhar and Susrut Banerjee, “Implementation of Human Action Recognition using Image Parsing Techniques”, 2018 Emerging Trends in Electronic Devices and Computational Techniques (EDCT), IEEE.
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      12. D. Aranki, G. Kurillo, P. Yan, D.M. Liebovitz, and R. Bajcsy, “Continuous, real-time, tele-monitoring of patients with chronic heart-failure: lessons learned from a pilot study,” in International Conference on Body Area Networks (Bodynets), 2014.
      13. Abouzar Ghasemi and C.N. Ravi Kumar, “A Novel Algorithm To Predict And Detect Suspicious Behaviors Of People At Public Areas For Surveillance Cameras”, International Conference on Intelligent Sustainable Systems (ICISS), 2017.
      14. Y. Nam, S. Rho, and J. Park, “G3d: A gaming action dataset and real time action recognition evaluation framework,” IEEE CVPR Workshops, 2012.
      15. Y. Nam, S. Rho, and J. Park, “Intelligent video surveillance system: 3-tier context-aware surveillance system with metadata,” Multimedia Tools and Applications, vol. 57, pp. 315–334, 2012.
      16. A. F. Bobick and J. W. Davis, “The recognition of human movement using temporal templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp. 257–267, 2001.
      17. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” 2005, pp. 1395–1402.
      18. I. Laptev and T. Lindeberg, “Local descriptors for spatio-temporal recognition,” in Spatial Coherence for Visual Motion Analysis, 2006.
      19. X. Yang, C. Zhang, and Y. Tian, “Recognizing actions using depth motion maps-based histograms of oriented gradients,” in ACM Multimedia (MM), 2012.
      20. J. Wang, Z. Liu, J. Chorowski, Z. Chen, and Y. Wu, “Robust 3d action recognition with random occupancy patterns,” in European Conference on Computer Vision (ECCV), 2012.
      21. X. Yang and Y. Tian, “Eigenjoints-based action recognition using naive Bayes nearest-neighbor,” in IEEE CVPR Workshops, 2012.

Citation

Swati Bhargava, Arun Jhapate, "Unusual Crowd Activity Detection Using Opencv and Motion Influence Map For Prior Appraisal Against Crime" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.2, pp.06-11, 2024.

A Review on Real time Drowsy Driver Detection in Digital Image Processing

Makhan Ahirwar, Aparna Singh Kushwah

Research Paper | Journal Paper

Vol.4, Issue.2, pp.12-17, Jun-2024

Abstract References Citation Download

Abstract

Consistently the amount of death and injuries are expanding in accidents because of human errors. Drowsiness while driving is a dangerous and it is exceptionally hard to distinguish. After liquor consumption; drowsiness is the subsequent driving reason for the street mishaps. People are conscious about the risk of drinking and driving but don’t realize the dangerous of drowsiness because no instruments exist to measure the driver drowsiness. On the off chance that the Driver neglecting to focus on driving it lessens the driver response time and hinder controlling conduct. Driver drowsiness can cause a several physical and economical losses. One approach to recognizing driver's drowsiness is to notice the driver with his driving, if driver not focusing on driving cautions the driver with the alert sound. In this paper, we survey and talk about the different identification strategies for distinguishing driver's drowsiness. There are several researches have been done till now but somewhere somehow they suffers due to accuracy especially at real time.

Key-Words / Index Term: Drowsiness Detection, Face Detection, Computer Vision, Support Vector Machine (SVM), OpenCV, Machine Learning, Non-Linear SVM Model.

References

    1. U.S. Department of Transportation, “Intelligent Vehicle Initiative 2002 annual report,” Report Link.
    2. National Highway Traffic Safety Administration, "Research on Drowsy Driving." Accessed October 20, 2015.
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    4. M. Dehnavi, N. Attarzadeh, and M. Eshghi, "Real time eye state recognition," IEEE, May 2011.
    5. Jaeik Jo, Sung Joo Lee, Ho Gi Jung, Ryoung Park, Jaihie Kim, "Vision based method for detecting driver drowsiness and distraction monitoring system," Optical Engineering, Vol. 50(12), Dec. 2011.
    6. LinkedIn, "Driver Drowsiness Detection Alert System with Open-CV & Keras Using IP-webCam," Available: Sep 2020, Link. Accessed: 29 June 2021.
    7. Fouzia, R. Roopalakshmi, J. A. Rathod, A. S. Shetty and K. Supriya, "Driver Drowsiness Detection System Based on Visual Features," 2018 ICICCT, pp. 1344-1347, doi:10.1109/ICICCT.2018.8473203.
    8. F. Guede-Fernández, M. Fernández-Chimeno, J. Ramos-Castro and M. A. García-González, "Driver Drowsiness Detection Based on Respiratory Signal Analysis," IEEE Access, vol. 7, pp. 81826-81838, 2019, doi:10.1109/ACCESS.2019.2924481.
    9. A. Bhaskar, "EyeAwake: A cost effective drowsy driver alert and vehicle correction system," 2017 ICIIECS, pp. 1-6, doi:10.1109/ICIIECS.2017.8276114.
    10. X. Ma, L. Chau and K. Yap, "Depth video-based two-stream convolutional neural networks for driver fatigue detection," 2017 ICOT, Singapore, pp. 155-158, doi:10.1109/ICOT.2017.8336111.
    11. A. Riztiane, D. H. Hareva, D. Stefani and S. Lukas, "Driver Drowsiness Detection Using Visual Information On Android Device," 2017 ICSIIT, Denpasar, pp. 283-287, doi:10.1109/ICSIIT.2017.20.
    12. S. Gupta, P. Jain and E. Rufus, "Drowsy Driver Alerting System," 2018 ICECA, pp. 1665-1670, doi:10.1109/ICECA.2018.8474931.
    13. F. You, X. Li, Y. Gong, H. Wang and H. Li, "A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration," IEEE Access, vol. 7, pp. 179396-179408, 2019, doi:10.1109/ACCESS.2019.2958667.
    14. B. M. K. Kumari and P. R. Kumar, "A survey on drowsy driver detection system," 2017 ICBDAC, pp. 272-279, doi:10.1109/ICBDACI.2017.8070847.
    15. A. Pinto, M. Bhasi, D. Bhalekar, P. Hegde and S. G. Koolagudi, "A Deep Learning Approach to Detect Drowsy Drivers in Real Time," 2019 INDICON, pp. 1-4, doi:10.1109/INDICON47234.2019.9030305.
    16. M. Miranda, A. Villanueva, M. J. Buo, R. Merabite, S. P. Perez and J. M. Rodriguez, "Portable Prevention and Monitoring of Driver’s Drowsiness Focuses to Eyelid Movement Using IoT," 2018 HNICEM, pp. 1-5, doi:10.1109/HNICEM.2018.8666334.
    17. Z. Jie, M. Mahmoud, Q. Stafford-Fraser, P. Robinson, E. Dias and L. Skrypchuk, "Analysis of Yawning Behaviour in Spontaneous Expressions of Drowsy Drivers," 2018 FG, pp. 571-576, doi:10.1109/FG.2018.00091.
    18. J. Yu, S. Park, S. Lee and M. Jeon, "Driver Drowsiness Detection Using Condition-Adaptive Representation Learning Framework," IEEE T-ITS, vol. 20, no. 11, pp. 4206-4218, 2019, doi:10.1109/TITS.2018.2883823.
    19. S. Kusuma, J. Divya Udayan and A. Sachdeva, "Driver Distraction Detection using Deep Learning and Computer Vision," 2019 ICICICT, pp. 289-292, doi:10.1109/ICICICT46008.2019.8993260.
    20. Y. Wang, L. Jin, K. Li, B. Guo, Y. Zheng and J. Shi, "Drowsy Driving Detection Based on Fused Data and Information Granulation," IEEE Access, vol. 7, pp. 183739-183750, 2019, doi:10.1109/ACCESS.2019.2960157.
    21. C. Yang, X. Wang and S. Mao, "Unsupervised Drowsy Driving Detection With RFID," IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8151-8163, Aug. 2020, doi:10.1109/TVT.2020.2995835.
    22. M. M. Islam, I. Kowsar, M. S. Zaman, M. F. Rahman Sakib and N. Saquib, "An Algorithmic Approach to Driver Drowsiness Detection for Ensuring Safety in an Autonomous Car," 2020 TENSYMP, pp. 328-333, doi:10.1109/TENSYMP50017.2020.9230766.

Citation

Makhan Ahirwar, Aparna Singh Kushwah, "A Review on Real time Drowsy Driver Detection in Digital Image Processing" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.2, pp.12-17, 2024.

Optical Character Reader & Text To Speech Conversion using Correlations & Speech Synthesis

Shivani Sonker, Avinash Rai

Research Paper | Journal Paper

Vol.4, Issue.2, pp.18-23, Jun-2024

Abstract References Citation Download

Abstract

In the modern era of image processing, recognizing content or information from an image is process of electronic conversion into machine encoded text. Advanced systems that are capable of producing high accuracy for multi-font recognition are now becoming commonplace, and with the support of digital consent formatting. Some programs are able to retrieve formats that are very close to the original page including images, columns, and other non-text items. Proposed system is able to recognize text from an image and convert it into editable text along with speech conversion. System uses Correlation model for OCR (Optical Character Recognition) and Speech Synthesis for TTS (Text To Speech) conversion. Correlation is a measurement of the similarities between two similar objects such as the predefined alphabets and recognizing a combination of those alphabets from an image. Speech synthesis is an artificial expression of human speech. The computer program that has been used this feature is called a speech computer as well as speech synthesizer that can be implemented on the basis of software or hardware primitives. The text-to-speech system (TTS) converts a standard language text into a speech; some programs provide figurative language presentations such as typed text in speech. System is capable enough to acquire high level of accuracy with less false recognition.

Key-Words / Index Term: OCR, TTS, Speech Synthesis, Correlation Model, Machine Encoding, Image Processing.

References

      1. Google Patents, OCR (Optical Character Recognition) and TTS (Text To Speech) based low-vision reading visual aid system, https://patents.google.com/patent/CN104966084A/en, Accessed- 07 July 2015.
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      3. S. C. Madre and S. B. Gundre, "OCR Based Image Text to Speech Conversion Using MATLAB," 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 858-861, doi:10.1109/ICCONS.2018.8663023.
      4. S. Gupta and S. Gupta, "Character Recognition and Speech Synthesis using Adaptive Neuro Fuzzy Inference System," 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida (UP), India, 2018, pp. 1091-1096, doi:10.1109/ICACCCN.2018.8748742.
      5. J. J. Mullani, M. Sankar, P. S. Khade, S. H. Sonalkar and N. L. Patil, "OCR BASED SPEECH SYNTHESIS SYSTEM USING LABVIEW: Text to Speech Conversion System using OCR," 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), Erode, 2018, pp. 7-14, doi:10.1109/ICCMC.2018.8487731.
      6. N. K. Sawant and S. Borkar, "Devanagari Printed Text to Speech Conversion using OCR," 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), Palladam, India, 2018, pp. 504-507, doi:10.1109/I-SMAC.2018.8653685.
      7. A. Mathur, A. Pathare, P. Sharma and S. Oak, "AI based Reading System for Blind using OCR," 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2019, pp. 39-42, doi:10.1109/ICECA.2019.8822226.
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      9. Amit Choudhary, Rahul Rishi, Savita Ahlawat, “Off-Line Handwritten Character Recognition using Features Extracted from Binarization Technique”, 2013 AASRI Conference on Intelligent Systems and Control.
      10. Pratik Madhukar Manwatkar and Shashank H. Yadav, “Text Recognition from Images”, IEEE Sponsored 2nd International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) 2015.
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Citation

Shivani Sonker, Avinash Rai "Optical Character Reader & Text To Speech Conversion using Correlations & Speech Synthesis" International Journal of Scientific Research in Technology & Management, Vol.4, Issue.2, pp.18-23, 2024.