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Moving object tracking using optical flow and motion vector estimation
Abstract
Moving object detection and tracking is an evolving research field due to its wide applications in traffic surveillance, 3D reconstruction, motion analysis (human and non-human), activity recognition, medical imaging etc. However real time object tracking is a challenging task due to dynamic tacking environment and different limiting parameters like view point, anthropometric variation, dimensions of an object, cluttered background, camera motions, occlusion etc. In this paper, we have developed new object detection and tracking algorithm which makes use of optical flow in conjunction with motion vector estimation for object detection and tracking in a sequence of frames. The optical flow gives valuable information about the object movement even if no quantitative parameters are computed. The motion vector estimation technique can provide an estimation of object position from consecutive frames which increases the accuracy of this algorithm and helps to provide robust result irrespective of image blur and cluttered background. The use of median filter with this algorithm makes it more robust in the presence of noise. The developed algorithm is applied to wide range of standard and real time datasets with different illumination (indoor and outdoor), object speed etc. The obtained results indicates that the developed algorithm over performs over conventional methods and state of art methods of object tracking.
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“They are useful in applications focused on movement patterns, such as monitoring crowd flows in and out.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [60]
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Metadata
- Authors
- Kale, K., Pawar, S., & Dhulekar, P.
- Affiliations
- Savitribai Phule Pune University (IN)
- Venue
- 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)
- Publisher
- IEEE
- Published
- 2015-09
- Pages
- 1-6
- OpenAlex
- W2221502561
- Cited by
- 103
- References
- 10
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 15 · 2023: 21 · 2022: 9 · 2021: 8 · 2020: 17 · 2019: 6 · 2018: 7 · 2017: 5 · 2016: 4
- FWCI
- 2.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- moving object tracking, moving object detection, optical flow, motion vector estimation, real-time tracking, median filter, noise robustness, background clutter, occlusion, traffic monitoring
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