Improving vehicle tracking rate and speed estimation in dusty and snowy weather conditions with a vibrating camera
Open accessAbstract
Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles is a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.), dusty weather in arid and semi-arid regions, at night, etc. Also, it is very important to consider speed of vehicles in the complicated weather condition. In this paper, we improved our method to track and count vehicles in dusty weather with vibrating camera. For this purpose, we used a background subtraction based strategy mixed with an extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a generalized particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result, with Centroid of each blob we calculated distance between two frames by simple formula and hence dividing it by the time between two frames obtained from the video. Our proposed method was tested on several video surveillance records in different conditions such as dusty or foggy weather, vibrating camera, and in roads with medium-level traffic volumes. The results showed that the new proposed method performed better than our previously published method and other methods, including the Kalman filter or Gaussian model, in different traffic conditions.
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The vehicle tracking and counting system in [113] for sandy weather with camera shaking had trouble identifying white automobiles in snowy settings.
“While the method worked well in difficult situations, such as evenings and sandy weather, with a vibrating camera, it had trouble identifying white automobiles in snowy settings.”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [113]
The Introduction and previous works section reports that an earlier method was robust in bright nights, snowy and dusty weather with a vibrating camera, but that white cars far from the camera were hard to detect in snow. It presents this as a limitation motivating improvements, not as a demonstrated limitation of the improved method in the cited paper. (LLM, conf=0.85)
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Cites
On-road vehicle detection: a review
Sun, Z., Bebis, G. N., & Miller, R. H. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.104 · Cited by 1,151 · see citing works
Fast multiple object tracking via a hierarchical particle filter
Yang, C., Ramani Duraiswami, B., & Davis, L. S. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.95 · Cited by 315 · see citing works
On-road vehicle detection using Gabor filters and support vector machines
Sun, Z., Bebis, G. N., & Miller, R. H. (2003) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2002.1028263 · Cited by 196 · see citing works
Embedded Vision System for Real-Time Object Tracking using an Asynchronous Transient Vision Sensor
Litzenberger, M., Posch, C., Bauer, D., Belbachir, A. N., Schon, P., Kohn, B., & Garn, H. (2006) · IEEE Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE) · doi:10.1109/dspws.2006.265448 · Cited by 166 · see citing works
Zhang, G., Avery, R. P., & Wang, Y. (2007) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1993-19 · Cited by 164 · see citing works
Vehicle detection and tracking under various lighting conditions using a particle filter
Chan, Y.-M., Huang, S.-S., Fu, L.-C., Hsiao, P., & Lo, M.-F. (2012) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2011.0019 · Cited by 66 · see citing works
Real Time Foreground-Background Segmentation Using a Modified Codebook Model
Ilyas, A., Scuturici, M., & Miguet, S. (2009) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2009.85 · Cited by 64 · see citing works
Tracking by cluster analysis of feature points using a mixture particle filter
Du, W. F., & Piater, J. (2006) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2005.1577261 · Cited by 37 · see citing works
Vehicle Tracking and Counting System in Dusty Weather with Vibrating Camera Conditions
Ershadi, N. Y., & Menéndez, J. M. (2017) · Journal of Sensors · doi:10.1155/2017/3812301 · Cited by 20 · see citing works
A Novel Omni-Directional Vision Sensing Technique for Traffic Surveillance
Cao, M., Vu, A., & Barth, M. (2007) · IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2007.4357784 · Cited by 17 · see citing works
Monocular line tracking for the reduction of vibration induced during image acquisition
Afolabi, D. O., Man, K. L., Liang, H., Guan, S.-U., & Krilavičius, T. (2015) · Journal of Vibroengineering · doi:10.21595/jve.2015.15677 · Cited by 8 · see citing works
Car tracking algorithm based on Kalman filter and compressive tracking
Li, H., Bai, P., & Song, H. (2014) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2014.7003744 · Cited by 7 · see citing works
Ikoma, N., Mikami, Y., & Ikenaga, T. (2014) · World Automation Congress (WAC) · doi:10.1109/wac.2014.6935890 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [5]Meshram S. A. and Raskar V. B. “ Vehicle detection and tracking techniques used in moving vehicles,” International Journal of Innovative Science, Engineering and Technology, vol. 2, no. 7, 2015.
- [6]Tripathi J., Chaudharya K., Joshia A., and Jawaleb J. B. “ Automatic vehicle counting and classification,” International Journal of Innovative and Emerging. Research in Engineering, vol. 2, no. 4, 2015.
Cited by
Sorted by citations
Tahir, N. U. A., Zhang, Z., Asim, M., Chen, J., & ELAffendi, M. (2024) · Algorithms · doi:10.3390/a17030103 · Cited by 145 · see citing works
Vehicles detection system at different weather conditions
Abdullah, M. N., & Ali, Y. H. (2021) · Iraqi Journal of Science · doi:10.24996/ijs.2021.62.6.30 · Cited by 14 · see citing works
Glare-Free High-Beam Control for Oncoming Vehicle Safety in Nighttime
Luo, C., Xin, G., Xu, H., & Tang, W. X. (2020) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2020.3040724 · Cited by 9 · see citing works
Ghosh, D. C., Bera, S., & Datta, P. K. (2022) · International Mobile and Embedded Technology Conference (MECON) · doi:10.1109/mecon53876.2022.9752182 · Cited by 3 · see citing works
Evaluating the effect of MIPM on vehicle detection performance
Ershadi, N. Y., Menéndez, J. M., & Jiménez, D. (2019) · Transportation Letters · doi:10.1080/19427867.2019.1585682 · Cited by 3 · see citing works
Robust vehicle detection under different weather conditions
Ershadi, N. Y. (2020) · doi:10.20868/upm.thesis.66186
Ershadi, N. Y., Menéndez, J. M., & Jiménez, D. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197446
A Wide-area Monitoring Technique of National Route using Visual IoT System
Kikuta, K., Murata, K. T., Nagatsuma, T., Tokairin, J., & Murakami, Y. (2026) · Journal of Information Processing · doi:10.2197/ipsjjip.34.368
- Authors
- Yaghoobi Ershadi, N.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-12-20
- Volume
- 12
- Issue
- 12
- Pages
- e0189145
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5738070
- OpenAlex
- W2780920736
- Cited by
- 8
- References
- 13
- Citations by year
- 2026: 1 · 2024: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 1
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Update notices
- correction (2018-05-10)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Media Technology › Vehicle License Plate Recognition
- Keywords
- vehicle tracking, vehicle speed estimation, vehicle counting, traffic monitoring, background subtraction, connected component analysis, centroid tracking
- MeSH terms
- Dust, Models, Theoretical, Photography, Snow, Vibration, Motor Vehicles
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