Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Real-Time Object Detection in Videos Using Deep Learning Models


Affiliations
1 Department of Master of Computer Applications, Cambridge Institute of Technology, India
2 Department of Electronics and Communication Engineering, Vaageswari College of Engineering, India
3 Department of Information Technology, St. Joseph’s College of Engineering, India
4 Department of Information Technology, Sandip Institute of Technology and Research Centre, India
     

   Subscribe/Renew Journal


Video object detection plays a pivotal role in various applications, from surveillance to autonomous vehicles. This research addresses the need for real-time object detection in videos using advanced deep learning models. The current landscape of object detection techniques often struggles to maintain efficiency in processing video streams, leading to delays and resource-intensive computations. This study aims to bridge this gap by proposing a novel methodology for real-time object detection in videos. With the surge in video data across domains, the demand for swift and accurate object detection in real-time has become imperative. Existing methods face challenges in balancing speed and precision, prompting the exploration of more robust solutions. This research endeavors to enhance the efficiency of video object detection, offering a timely and accurate approach to address contemporary demands. The primary challenge lies in achieving real-time object detection without compromising accuracy. Traditional methods often compromise speed for precision, leading to inadequate performance in dynamic video environments. This study seeks to overcome this dilemma by introducing a methodology that optimizes both speed and accuracy, catering to the real-time constraints of video processing. Despite the advancements in object detection, a notable research gap exists in the domain of real-time video object detection. Existing models exhibit limitations in adapting to the dynamic nature of video streams, necessitating the development of novel methodologies. This research aims to fill this void by proposing an innovative approach that addresses the specific challenges posed by real-time video data. The proposed methodology integrates state-of-the-art deep learning models, optimizing them for real-time video object detection. Leveraging advanced architectures and streamlining the inference process, the model aims to provide accurate detections at unparalleled speeds. Additionally, a novel data augmentation technique is introduced to enhance the model’s adaptability to dynamic video scenarios. Preliminary results demonstrate the effectiveness of the proposed methodology, showcasing a significant improvement in both real-time processing speed and object detection accuracy. The model exhibits promising performance across diverse video datasets, highlighting its potential to outperform existing methods in real-world applications.

Keywords

Real-Time Object Detection, Deep Learning, Video Analysis, Computer Vision, Model Optimization
Subscription Login to verify subscription
User
Notifications
Font Size

  • G. Chandan and H. Jain, “Real Time Object Detection and Tracking using Deep Learning and OpenCV”, Proceedings of International Conference on Inventive Research in Computing Applications, pp. 1305-1308,
  • M. Bhende and S. Shinde, “Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection”, BioMed Research International, Vol. 2022, pp. 1-12, 2022.
  • A. Younis and Z. Hai, “Real-Time Object Detection using Pre-Trained Deep Learning Models Mobile Net-SSD”, Proceedings of International Conference on Computing and Data Engineering, pp. 44-48, 2020.
  • A.G. Ismaeel, M. Sankar and A.H. Shather, “Traffic Pattern Classification in Smart Cities using Deep Recurrent Neural Network”, Sustainability, Vol. 15, No. 19, pp. 14522-14532, 2023.
  • C.B. Murthy and Z.W. Geem, “Investigations of Object Detection in Images/Videos using Various Deep Learning Techniques and Embedded Platforms-A Comprehensive Review”, Applied sciences, Vol. 10, No. 9, pp. 3280-3289, 2020.
  • S. Jha and G.P. Joshi, “Real Time Object Detection and Tracking System for Video Surveillance System”, Multimedia Tools and Applications, Vol. 80, pp. 3981-3996, 2021.
  • S. Gupta and K.S. Babu, “Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-based Neurodegenerative Disorders”, Computational and Mathematical Methods in Medicine, Vol. 2022, pp. 1-8, 2022.
  • M. Mohseni, A.B. Mishra and S.J. Priya, “The Role of Parallel Computing Towards Implementation of Enhanced and Effective Industrial Internet of Things (IOT) Through Manova Approach”, Proceedings of International Conference on Advance Computing and Innovative Technologies in Engineering, pp. 160-164, 2022.
  • G. Kiruthiga, “Improved Object Detection in Video Surveillance using Deep Convolutional Neural Network Learning”, International Journal for Modern Trends in Science and Technology, Vol. 7, No. 11, pp. 104-108, 2021.
  • M.T. Bhatti and M.J. Fiaz, “Weapon Detection in Real-Time CCTV Videos using Deep Learning”, IEEE Access, Vol. 9, pp. 34366-34382, 2021.
  • R. Pavithra and V. Saravanan, “Web Service Deployment for Selecting a Right Steganography Scheme for Optimizing both the Capacity and the Detectable Distortion”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 6, No. 4, pp. 267-277, 2018.
  • K. Praghash, S. Chidambaram and D. Shreecharan, “Hyperspectral Image Classification using Denoised Stacked Auto Encoder-Based Restricted Boltzmann Machine Classifier”, Proceedings of International Conference on Hybrid Intelligent Systems, pp. 213-221, 2022.
  • S. Silvia Priscila, C. Sathish Kumar and R. Manikandan, “Interactive Artificial Neural Network Model for UX Design”, Proceedings of International Conference on Computing, Communication, Electrical and Biomedical Systems, pp. 277-284, 2022.
  • Z. Chen, A. Atahouet and J.Y. Ertaud, “Real Time Object Detection, Tracking, and Distance and Motion Estimation based on Deep Learning: Application to Smart Mobility”, Proceedings of International Conference on Emerging Security Technologies, pp. 1-6, 2019.
  • A. Juneja and S. Jain, “Real Time Object Detection using CNN based Single Shot Detector Model”, Journal of Information Technology Management, Vol. 13, No. 1, pp. 62-80, 2021.
  • Y.C. Hou and S. Dzulkifly, “Social Distancing Detection with Deep Learning Model”, Proceedings of International Conference on Information Technology and Multimedia, pp. 334-338, 2020.

Abstract Views: 57

PDF Views: 1




  • Real-Time Object Detection in Videos Using Deep Learning Models

Abstract Views: 57  |  PDF Views: 1

Authors

M. Monika
Department of Master of Computer Applications, Cambridge Institute of Technology, India
Udutha Rajender
Department of Electronics and Communication Engineering, Vaageswari College of Engineering, India
A. Tamizhselvi
Department of Information Technology, St. Joseph’s College of Engineering, India
Aniruddha S. Rumale
Department of Information Technology, Sandip Institute of Technology and Research Centre, India

Abstract


Video object detection plays a pivotal role in various applications, from surveillance to autonomous vehicles. This research addresses the need for real-time object detection in videos using advanced deep learning models. The current landscape of object detection techniques often struggles to maintain efficiency in processing video streams, leading to delays and resource-intensive computations. This study aims to bridge this gap by proposing a novel methodology for real-time object detection in videos. With the surge in video data across domains, the demand for swift and accurate object detection in real-time has become imperative. Existing methods face challenges in balancing speed and precision, prompting the exploration of more robust solutions. This research endeavors to enhance the efficiency of video object detection, offering a timely and accurate approach to address contemporary demands. The primary challenge lies in achieving real-time object detection without compromising accuracy. Traditional methods often compromise speed for precision, leading to inadequate performance in dynamic video environments. This study seeks to overcome this dilemma by introducing a methodology that optimizes both speed and accuracy, catering to the real-time constraints of video processing. Despite the advancements in object detection, a notable research gap exists in the domain of real-time video object detection. Existing models exhibit limitations in adapting to the dynamic nature of video streams, necessitating the development of novel methodologies. This research aims to fill this void by proposing an innovative approach that addresses the specific challenges posed by real-time video data. The proposed methodology integrates state-of-the-art deep learning models, optimizing them for real-time video object detection. Leveraging advanced architectures and streamlining the inference process, the model aims to provide accurate detections at unparalleled speeds. Additionally, a novel data augmentation technique is introduced to enhance the model’s adaptability to dynamic video scenarios. Preliminary results demonstrate the effectiveness of the proposed methodology, showcasing a significant improvement in both real-time processing speed and object detection accuracy. The model exhibits promising performance across diverse video datasets, highlighting its potential to outperform existing methods in real-world applications.

Keywords


Real-Time Object Detection, Deep Learning, Video Analysis, Computer Vision, Model Optimization

References