Detecting and Tracking Vehicles in Airborne Videos
In this work, we present an automatic vehicle detection
system for airborne videos using combined features. We propose a
pixel-wise classification method for vehicle detection using Dynamic
Bayesian Networks. In spite of performing pixel-wise classification,
relations among neighboring pixels in a region are preserved in the
feature extraction process. The main novelty of the detection scheme is
that the extracted combined features comprise not only pixel-level
information but also region-level information. Afterwards, tracking is
performed on the detected vehicles. Tracking is performed using
efficient Kalman filter with dynamic particle sampling. Experiments
were conducted on a wide variety of airborne videos. We do not
assume prior information of camera heights, orientation, and target
object sizes in the proposed framework. The results demonstrate
flexibility and good generalization abilities of the proposed method on
a challenging dataset.
[1] R. Lin, X. Cao, Y. Xu, C. Wu, and H. Qiao, "Airborne moving vehicle
detection for urban traffic surveillance," Proceedings of the 11th
International IEEE Conference on Intelligent Transportation Systems,
Oct. 2008, pp. 163-167.
[2] R. Lin, X. Cao, Y. Xu, C. Wu, and H. Qiao, "Airborne moving vehicle
detection for video surveillance of urban traffic," IEEE Intelligent
Vehicles Symposium, 2009, pp. 203-208.
[3] J.Y. Choi and Y.K. Yang, "Vehicle detection from aerial images using
local shape information," Lecture Notes in Computer Science, vol. 5414,
Jan. 2009, pp. 227-236.
[4] C.G. Harris and M.J. Stephens, "A combined corner and edge detector,"
Proceedings of the 4th Alvey Vision Conference, 1988, p.147-151.
[5] W.H. Tsai, "Moment-preserving thresholding: a new approach,"
Computer Vision Graphics, and Image Processing, vol. 29, no. 3, pp.
377-393, 1985.
[6] J.F.Canny, "A Computational Approach to Edge Detection," IEEE Trans.
Pattern Analysis and Machine Intelligence," vol. 8, no. 6, pp. 679-698,
1986.
[7] L.W. Tsai, J.W. Hsieh, and K.C. Fan, "Vehicle detection using
normalized color and edge map," IEEE Trans. on Image Processing, vol.
16, no. 3, 2007.
[8] S. Russell, P. Norvig, "Artificial intelligence: a modern approach (second
edition)," Prentice Hall, 2003.
[9] H. Y. Cheng and J. N. Hwang, "Multiple-target tracking for crossroad
traffic utilizing modified probabilistic data association," 2007 IEEE
International Conf. on Acoustics, Speech, and Signal Processing
(ICASSP), Honolulu, Hawaii, (2007).
[10] H. Y. Cheng and J. N. Hwang, "Adaptive particle sampling and adaptive
appearance for multiple video object tracking," Signal Processing, vol.
89, no. 9, pp. 1844-1849, Sep. 2009.
[1] R. Lin, X. Cao, Y. Xu, C. Wu, and H. Qiao, "Airborne moving vehicle
detection for urban traffic surveillance," Proceedings of the 11th
International IEEE Conference on Intelligent Transportation Systems,
Oct. 2008, pp. 163-167.
[2] R. Lin, X. Cao, Y. Xu, C. Wu, and H. Qiao, "Airborne moving vehicle
detection for video surveillance of urban traffic," IEEE Intelligent
Vehicles Symposium, 2009, pp. 203-208.
[3] J.Y. Choi and Y.K. Yang, "Vehicle detection from aerial images using
local shape information," Lecture Notes in Computer Science, vol. 5414,
Jan. 2009, pp. 227-236.
[4] C.G. Harris and M.J. Stephens, "A combined corner and edge detector,"
Proceedings of the 4th Alvey Vision Conference, 1988, p.147-151.
[5] W.H. Tsai, "Moment-preserving thresholding: a new approach,"
Computer Vision Graphics, and Image Processing, vol. 29, no. 3, pp.
377-393, 1985.
[6] J.F.Canny, "A Computational Approach to Edge Detection," IEEE Trans.
Pattern Analysis and Machine Intelligence," vol. 8, no. 6, pp. 679-698,
1986.
[7] L.W. Tsai, J.W. Hsieh, and K.C. Fan, "Vehicle detection using
normalized color and edge map," IEEE Trans. on Image Processing, vol.
16, no. 3, 2007.
[8] S. Russell, P. Norvig, "Artificial intelligence: a modern approach (second
edition)," Prentice Hall, 2003.
[9] H. Y. Cheng and J. N. Hwang, "Multiple-target tracking for crossroad
traffic utilizing modified probabilistic data association," 2007 IEEE
International Conf. on Acoustics, Speech, and Signal Processing
(ICASSP), Honolulu, Hawaii, (2007).
[10] H. Y. Cheng and J. N. Hwang, "Adaptive particle sampling and adaptive
appearance for multiple video object tracking," Signal Processing, vol.
89, no. 9, pp. 1844-1849, Sep. 2009.
@article{"International Journal of Information, Control and Computer Sciences:58207", author = "Hsu-Yung Cheng and Chih-Chang Yu", title = "Detecting and Tracking Vehicles in Airborne Videos", abstract = "In this work, we present an automatic vehicle detection
system for airborne videos using combined features. We propose a
pixel-wise classification method for vehicle detection using Dynamic
Bayesian Networks. In spite of performing pixel-wise classification,
relations among neighboring pixels in a region are preserved in the
feature extraction process. The main novelty of the detection scheme is
that the extracted combined features comprise not only pixel-level
information but also region-level information. Afterwards, tracking is
performed on the detected vehicles. Tracking is performed using
efficient Kalman filter with dynamic particle sampling. Experiments
were conducted on a wide variety of airborne videos. We do not
assume prior information of camera heights, orientation, and target
object sizes in the proposed framework. The results demonstrate
flexibility and good generalization abilities of the proposed method on
a challenging dataset.", keywords = "Vehicle Detection, Airborne Video, Tracking,
Dynamic Bayesian Networks", volume = "6", number = "5", pages = "631-4", }