Research Article
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Year 2022, , 174 - 183, 30.06.2022
https://doi.org/10.47000/tjmcs.1002767

Abstract

References

  • Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P. et al., SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Transactions On Pattern Analysis And Machine Intelligence, 34(2012), 2274-2282.
  • Allebosch, G., Deboeverie, F., Veelaert, P., Philips, W., EFIC: edge based foreground background segmentation and interior classification for dynamic camera viewpoints, International Conference On Advanced Concepts For Intelligent Vision Systems, (2015), 130-141.
  • Berker Logoglu, K., Lezki, H., Kerim Yucel, M., Ozturk, A., Kucukkomurler, A. et al., Feature-based efficient moving object detection for low-altitude aerial platforms, Proceedings Of The IEEE International Conference On Computer Vision Workshops, (2017), 2119-2128.
  • Bochkovskiy, A., Wang, C., Liao, H., Yolov4: Optimal speed and accuracy of object detection, ArXiv Preprint ArXiv:2004.10934, (2020).
  • Bouwmans, T., Hofer-lin, B., Porikli, F., Vacavant, A., Traditional Approaches in Background Modeling for Video Surveillance, Handbook Background Modeling And Foreground Detection For Video Surveillance, Taylor And Francis Group, 2014.
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  • De Gregorio, M., Giordano, M., WiSARDrp for Change Detection in Video Sequences, ESANN, 2017.
  • Delibasoglu, I., UAV images dataset for moving object detection from moving cameras, ArXiv E-prints, earXiv:2103.11460, (2021).
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  • Moo Yi, K., Yun, K., Wan Kim, S., Jin Chang, H., Young Choi, J., Detection of moving objects with non-stationary cameras in 5.8 ms: Bringing motion detection to your mobile device, Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition Workshops, (2013), 27-34.
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  • Redmon, J., Farhadi, A., Yolov3: An incremental improvement, ArXiv Preprint ArXiv:1804.02767, (2018).
  • Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: Towards real-time object detection with region proposal networks, ArXiv Preprint ArXiv:1506.01497, (2015).
  • Ren, C., Prisacariu, V., Reid, I., gSLICr: SLIC superpixels at over 250Hz, ArXiv E-prints, (2015).
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. et al., Going deeper with convolutions, Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition, (2015), 1-9.
  • Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition, ArXiv Preprint ArXiv:1409.1556, (2014).
  • Yun, K., Lim, J., Choi, J., Scene conditional background update for moving object detection in a moving camera, Pattern Recognition Letters, 88(2017), 57-63.
  • Zhao, L., Tong, Q., Wang, H., Study on moving-object-detection arithmetic based on W4 theory, 2011 2nd International Conference On Artificial Intelligence, Management Science And Electronic Commerce (AIMSEC), (2011), 4387-4390.
  • Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q., Vision meets drones: A challenge, ArXiv Preprint ArXiv:1804.07437, (2018).
  • Zivkovic, Z., Improved adaptive Gaussian mixture model for background subtraction, Proceedings Of The 17th International Conference On Pattern Recognition, ICPR 2004, 2(2004), 28-31.
  • Zivkovic, Z., Van Der Heijden, F., Efficient adaptive density estimation per image pixel for the task of background subtraction, Pattern Recognition Letters, 27(2006), 773-780.

Vehicle Detection from Aerial Images with Object and Motion Detection

Year 2022, , 174 - 183, 30.06.2022
https://doi.org/10.47000/tjmcs.1002767

Abstract

Moving vehicle detection is one of important issues in surveillance and traffic monitoring applications for aerial images. In this study, a vehicle detection method is proposed by combining motion and object detection. A method based on background modeling and subtraction is applied for motion detection, while Faster-RCNN architecture is used for object detection. Motion detection result is enhanced with the proposed superpixel based refinement method. Experimental study shows that performance of motion detection increases about 8\% for $F_1$ metric with the proposed post processing method. Object detection, motion detection and superpixel segmentation methods interact with each other in parallel processes with the proposed software architecture, which significantly increases the working speed of the method. In last step of the proposed method, each vehicle is tracked with the kalman filter. The performance of proposed method is evaluated on the VIVID dataset. The performance evaluation shows that proposed method increases $F_1$ and recall values significantly compared to the motion and object detection methods alone. It also outperforms SCBU and MCD methods which are widely used for performance comparison in motion detection studies in the literature

References

  • Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P. et al., SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Transactions On Pattern Analysis And Machine Intelligence, 34(2012), 2274-2282.
  • Allebosch, G., Deboeverie, F., Veelaert, P., Philips, W., EFIC: edge based foreground background segmentation and interior classification for dynamic camera viewpoints, International Conference On Advanced Concepts For Intelligent Vision Systems, (2015), 130-141.
  • Berker Logoglu, K., Lezki, H., Kerim Yucel, M., Ozturk, A., Kucukkomurler, A. et al., Feature-based efficient moving object detection for low-altitude aerial platforms, Proceedings Of The IEEE International Conference On Computer Vision Workshops, (2017), 2119-2128.
  • Bochkovskiy, A., Wang, C., Liao, H., Yolov4: Optimal speed and accuracy of object detection, ArXiv Preprint ArXiv:2004.10934, (2020).
  • Bouwmans, T., Hofer-lin, B., Porikli, F., Vacavant, A., Traditional Approaches in Background Modeling for Video Surveillance, Handbook Background Modeling And Foreground Detection For Video Surveillance, Taylor And Francis Group, 2014.
  • Collins, R., Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D. et al., Others A system for video surveillance and monitoring, VSAM Final Report, (2000), 1.
  • Collins, R., Zhou, X., Teh, S., An open source tracking testbed and evaluation web site, IEEE International Workshop On Performance Evaluation Of Tracking And Surveillance, 2(2005), 35.
  • De Gregorio, M., Giordano, M., WiSARDrp for Change Detection in Video Sequences, ESANN, 2017.
  • Delibasoglu, I., UAV images dataset for moving object detection from moving cameras, ArXiv E-prints, earXiv:2103.11460, (2021).
  • Girshick, R., Fast r-cnn, Proceedings Of The IEEE International Conference On Computer Vision, (2015), 1440-1448.
  • Girshick, R., Donahue, J., Darrell, T., Malik, J., Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition, (2014), 580-587.
  • He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition, Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition. (2016), 770-778.
  • Huang, J., Zou, W., Zhu, J., Zhu, Z., Optical flow based real-time moving object detection in unconstrained scenes, ArXiv Preprint ArXiv:1807.04890, (2018).
  • Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy et al., Flownet 2.0: Evolution of optical flow estimation with deep networks, Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition, (2017), 2462-2470.
  • Krizhevsky, A., Sutskever, I., Hinton, G., Imagenet classification with deep convolutional neural networks, Advances In Neural Information Processing Systems, 25(2012), 1097-1105.
  • Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S. et al., Ssd: Single shot multibox detector, European Conference On Computer Vision, (2016), 21-37.
  • Moo Yi, K., Yun, K., Wan Kim, S., Jin Chang, H., Young Choi, J., Detection of moving objects with non-stationary cameras in 5.8 ms: Bringing motion detection to your mobile device, Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition Workshops, (2013), 27-34.
  • Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You only look once: Unified, real-time object detection, Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition, (2016), 779-788.
  • Redmon, J., Farhadi, A., YOLO9000: better, faster, stronger, Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition, (2017), 7263-7271.
  • Redmon, J., Farhadi, A., Yolov3: An incremental improvement, ArXiv Preprint ArXiv:1804.02767, (2018).
  • Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: Towards real-time object detection with region proposal networks, ArXiv Preprint ArXiv:1506.01497, (2015).
  • Ren, C., Prisacariu, V., Reid, I., gSLICr: SLIC superpixels at over 250Hz, ArXiv E-prints, (2015).
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. et al., Going deeper with convolutions, Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition, (2015), 1-9.
  • Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition, ArXiv Preprint ArXiv:1409.1556, (2014).
  • Yun, K., Lim, J., Choi, J., Scene conditional background update for moving object detection in a moving camera, Pattern Recognition Letters, 88(2017), 57-63.
  • Zhao, L., Tong, Q., Wang, H., Study on moving-object-detection arithmetic based on W4 theory, 2011 2nd International Conference On Artificial Intelligence, Management Science And Electronic Commerce (AIMSEC), (2011), 4387-4390.
  • Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q., Vision meets drones: A challenge, ArXiv Preprint ArXiv:1804.07437, (2018).
  • Zivkovic, Z., Improved adaptive Gaussian mixture model for background subtraction, Proceedings Of The 17th International Conference On Pattern Recognition, ICPR 2004, 2(2004), 28-31.
  • Zivkovic, Z., Van Der Heijden, F., Efficient adaptive density estimation per image pixel for the task of background subtraction, Pattern Recognition Letters, 27(2006), 773-780.
There are 29 citations in total.

Details

Primary Language English
Subjects Software Engineering
Journal Section Articles
Authors

İbrahim Delibaşoğlu 0000-0001-8119-2873

Publication Date June 30, 2022
Published in Issue Year 2022

Cite

APA Delibaşoğlu, İ. (2022). Vehicle Detection from Aerial Images with Object and Motion Detection. Turkish Journal of Mathematics and Computer Science, 14(1), 174-183. https://doi.org/10.47000/tjmcs.1002767
AMA Delibaşoğlu İ. Vehicle Detection from Aerial Images with Object and Motion Detection. TJMCS. June 2022;14(1):174-183. doi:10.47000/tjmcs.1002767
Chicago Delibaşoğlu, İbrahim. “Vehicle Detection from Aerial Images With Object and Motion Detection”. Turkish Journal of Mathematics and Computer Science 14, no. 1 (June 2022): 174-83. https://doi.org/10.47000/tjmcs.1002767.
EndNote Delibaşoğlu İ (June 1, 2022) Vehicle Detection from Aerial Images with Object and Motion Detection. Turkish Journal of Mathematics and Computer Science 14 1 174–183.
IEEE İ. Delibaşoğlu, “Vehicle Detection from Aerial Images with Object and Motion Detection”, TJMCS, vol. 14, no. 1, pp. 174–183, 2022, doi: 10.47000/tjmcs.1002767.
ISNAD Delibaşoğlu, İbrahim. “Vehicle Detection from Aerial Images With Object and Motion Detection”. Turkish Journal of Mathematics and Computer Science 14/1 (June 2022), 174-183. https://doi.org/10.47000/tjmcs.1002767.
JAMA Delibaşoğlu İ. Vehicle Detection from Aerial Images with Object and Motion Detection. TJMCS. 2022;14:174–183.
MLA Delibaşoğlu, İbrahim. “Vehicle Detection from Aerial Images With Object and Motion Detection”. Turkish Journal of Mathematics and Computer Science, vol. 14, no. 1, 2022, pp. 174-83, doi:10.47000/tjmcs.1002767.
Vancouver Delibaşoğlu İ. Vehicle Detection from Aerial Images with Object and Motion Detection. TJMCS. 2022;14(1):174-83.