A Practical and Efficient Evaluation Function for 3D Model Based Vehicle Matching
3D model-based vehicle matching provides a new way
for vehicle recognition, localization and tracking. Its key is to
construct an evaluation function, also called fitness function, to
measure the degree of vehicle matching. The existing fitness functions
often poorly perform when the clutter and occlusion exist in traffic
scenarios. In this paper, we present a practical and efficient fitness
function. Unlike the existing evaluation functions, the proposed
fitness function is to study the vehicle matching problem from
both local and global perspectives, which exploits the pixel gradient
information as well as the silhouette information. In view of the
discrepancy between 3D vehicle model and real vehicle, a weighting
strategy is introduced to differently treat the fitting of the model’s
wireframes. Additionally, a normalization operation for the model’s
projection is performed to improve the accuracy of the matching.
Experimental results on real traffic videos reveal that the proposed
fitness function is efficient and robust to the cluttered background
and partial occlusion.
[1] P. David and D. DeMenthon, “Object recognition in high clutter images
using line features,” in Computer Vision, 2005. ICCV 2005. Tenth IEEE
International Conference on, vol. 2. IEEE, 2005, pp. 1581–1588.
[2] C. Wiedemann, M. Ulrich, and C. Steger, “Recognition and tracking of
3d objects,” Pattern Recognition, pp. 132–141, 2008.
[3] Y. Guo, C. Rao, S. Samarasekera, J. Kim, R. Kumar, and H. Sawhney,
“Matching vehicles under large pose transformations using approximate
3d models and piecewise mrf model,” in Computer Vision and Pattern
Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008, pp.
1–8.
[4] S. Nedevschi, S. Bota, and C. Tomiuc, “Stereo-based pedestrian
detection for collision-avoidance applications,” Intelligent
Transportation Systems, IEEE Transactions on, vol. 10, no. 3,
pp. 380–391, 2009.
[5] J. Liebelt and K. Schertler, “Precise registration of 3d models to images
by swarming particles,” in Computer Vision and Pattern Recognition,
2007. CVPR’07. IEEE Conference on. IEEE, 2007, pp. 1–8.
[6] B. Rosenhahn, T. Brox, and J. Weickert, “Three-dimensional
shape knowledge for joint image segmentation and pose tracking,”
International Journal of Computer Vision, vol. 73, no. 3, pp. 243–262,
2007.
[7] S. Dambreville, R. Sandhu, A. Yezzi, and A. Tannenbaum, “Robust 3d
pose estimation and efficient 2d region-based segmentation from a 3d
shape prior,” in Computer Vision–ECCV 2008. Springer, 2008, pp.
169–182.
[8] C. Reinbacher, M. R¨uther, and H. Bischof, “Pose estimation of known
objects by efficient silhouette matching,” in Proceedings of the 2010
20th International Conference on Pattern Recognition. IEEE Computer
Society, 2010, pp. 1080–1083.
[9] V. Prisacariu and I. Reid, “Pwp3d: Real-time segmentation and tracking
of 3d objects,” International Journal of Computer Vision, pp. 1–20,
2012.
[10] C. Meilhac and C. Nastar, “Robust fitting of 3d cad models to video
streams,” in Image Analysis and Processing. Springer, 1997, pp.
661–668.
[11] E. Rosten and T. Drummond, “Fusing points and lines for high
performance tracking,” in Computer Vision, 2005. ICCV 2005. Tenth
IEEE International Conference on, vol. 2. IEEE, 2005, pp. 1508–1515.
[12] S. Hinterstoisser, S. Benhimane, and N. Navab, “N3m: Natural 3d
markers for real-time object detection and pose estimation,” in Computer
Vision, 2007. ICCV 2007. IEEE 11th International Conference on.
IEEE, 2007, pp. 1–7. [13] A. Del Bue, “Adaptive metric registration of 3d models to non-rigid
image trajectories,” Computer Vision–ECCV 2010, pp. 87–100, 2010.
[14] S. M. Khan, H. Cheng, D. Matthies, and H. Sawhney, “3d model based
vehicle classification in aerial imagery,” in Computer Vision and Pattern
Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010, pp.
1681–1687.
[15] E. Marchand, P. Bouthemy, F. Chaumette, V. Moreau et al., “Robust
real-time visual tracking using a 2d-3d model-based approach,” in IEEE
Int. Conf. on Computer Vision, ICCV’99, vol. 1, 1999, pp. 262–268.
[16] C. Schmaltz, B. Rosenhahn, T. Brox, D. Cremers, J. Weickert,
L. Wietzke, and G. Sommer, “Region-based pose tracking,” in Pattern
Recognition and Image Analysis. Springer, 2007, pp. 56–63.
[17] S. Messelodi, C. Modena, and M. Zanin, “A computer vision system for
the detection and classification of vehicles at urban road intersections,”
Pattern Analysis & Applications, vol. 8, no. 1, pp. 17–31, 2005.
[18] N. Buch, J. Orwell, and S. Velastin, “Urban road user detection and
classification using 3d wire frame models,” Computer Vision, IET, vol. 4,
no. 2, pp. 105–116, 2010.
[19] Z. Zhang, T. Tan, K. Huang, and Y. Wang, “Three-dimensional
deformable-model-based localization and recognition of road vehicles,”
Image Processing, IEEE Transactions on, vol. 21, no. 1, pp. 1–13, 2012.
[20] H. Kollnig and H. Nagel, “3d pose estimation by directly matching
polyhedral models to gray value gradients,” International Journal of
Computer Vision, vol. 23, no. 3, pp. 283–302, 1997.
[21] T. Tan and K. Baker, “Efficient image gradient based vehicle
localization,” Image Processing, IEEE Transactions on, vol. 9, no. 8,
pp. 1343–1356, 2000.
[22] D. Roller, K. Daniilidis, and H. Nagel, “Model-based object tracking in
monocular image sequences of road traffic scenes,” International Journal
of Computer Vision, vol. 10, no. 3, pp. 257–281, 1993.
[23] M. Haag and H. Nagel, “Combination of edge element and optical
flow estimates for 3d-model-based vehicle tracking in traffic image
sequences,” International Journal of Computer Vision, vol. 35, no. 3,
pp. 295–319, 1999.
[24] J. Lou, T. Tan, W. Hu, H. Yang, and S. Maybank, “3-d model-based
vehicle tracking,” Image Processing, IEEE Transactions on, vol. 14,
no. 10, pp. 1561–1569, 2005.
[25] M. J. Leotta and J. L. Mundy, “Vehicle surveillance with a generic,
adaptive, 3d vehicle model,” Pattern Analysis and Machine Intelligence,
IEEE Transactions on, vol. 33, no. 7, pp. 1457–1469, 2011.
[26] B. Yan, S. Wang, Y. Chen, and X. Ding, “Deformable 3-d model
based vehicle matching with weighted hausdorff and eda in traffic
surveillance,” in Image Analysis and Signal Processing (IASP), 2010
International Conference on. IEEE, 2010, pp. 22–27.
[27] M. Hodlmoser, B. Micusik, M. Y. Liu, M. Pollefeys, and M. Kampel,
“Classification and pose estimation of vehicles in videos by 3d modeling
within discrete-continuous optimization,” in 3D Imaging, Modeling,
Processing, Visualization and Transmission (3DIMPVT), 2012 Second
International Conference on. IEEE, 2012, pp. 198–205.
[28] S. Gupte, O. Masoud, R. Martin, and N. Papanikolopoulos, “Detection
and classification of vehicles,” Intelligent Transportation Systems, IEEE
Transactions on, vol. 3, no. 1, pp. 37–47, 2002.
[29] Y. Tsin, Y. Genc, and V. Ramesh, “Explicit 3d modeling for vehicle
monitoring in non-overlapping cameras,” in Advanced Video and
Signal Based Surveillance, 2009. AVSS’09. Sixth IEEE International
Conference on. IEEE, 2009, pp. 110–115.
[30] Q. Liu, J. Lou, W. Hu, and T. Tan, “Pose evaluation based on
bayesian classification error,” in In Proc. of 14th British Machine Vision
Conference, 2003, pp. 409–418.
[31] B. Johansson, J. Wiklund, P. Forss´en, and G. Granlund, “Combining
shadow detection and simulation for estimation of vehicle size and
position,” Pattern Recognition Letters, vol. 30, no. 8, pp. 751–759, 2009.
[32] S. Jayawardena, M. Hutter, and N. Brewer, “Featureless 2d–3d pose
estimation by minimising an illumination-invariant loss,” in Image
and Vision Computing New Zealand (IVCNZ), 2010 25th International
Conference of. IEEE, 2010, pp. 1–8. [33] T. Hou, S. Wang, and H. Qin, “Vehicle matching and recognition under
large variations of pose and illumination,” in Computer Vision and
Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE
Computer Society Conference on. IEEE, 2009, pp. 24–29.
[34] K. Brisdon, “Hypothesis verification using iconic matching.” Ph.D.
dissertation, University of Reading, 1990.
[35] A. Pece and A. Worrall, “Tracking without feature detection,” in
Proc. IEEE Int. Workshop Performance Evaluation of Tracking and
Surveillance. Citeseer, 2000, pp. 29–37.
[36] M. Hodlmoser, B. Micusik, M. Pollefeys, M.-Y. Liu, and M. Kampel,
“Model-based vehicle pose estimation and tracking in videos using
random forests,” in 3DTV-Conference, 2013 International Conference
on. IEEE, 2013, pp. 430–437.
[1] P. David and D. DeMenthon, “Object recognition in high clutter images
using line features,” in Computer Vision, 2005. ICCV 2005. Tenth IEEE
International Conference on, vol. 2. IEEE, 2005, pp. 1581–1588.
[2] C. Wiedemann, M. Ulrich, and C. Steger, “Recognition and tracking of
3d objects,” Pattern Recognition, pp. 132–141, 2008.
[3] Y. Guo, C. Rao, S. Samarasekera, J. Kim, R. Kumar, and H. Sawhney,
“Matching vehicles under large pose transformations using approximate
3d models and piecewise mrf model,” in Computer Vision and Pattern
Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008, pp.
1–8.
[4] S. Nedevschi, S. Bota, and C. Tomiuc, “Stereo-based pedestrian
detection for collision-avoidance applications,” Intelligent
Transportation Systems, IEEE Transactions on, vol. 10, no. 3,
pp. 380–391, 2009.
[5] J. Liebelt and K. Schertler, “Precise registration of 3d models to images
by swarming particles,” in Computer Vision and Pattern Recognition,
2007. CVPR’07. IEEE Conference on. IEEE, 2007, pp. 1–8.
[6] B. Rosenhahn, T. Brox, and J. Weickert, “Three-dimensional
shape knowledge for joint image segmentation and pose tracking,”
International Journal of Computer Vision, vol. 73, no. 3, pp. 243–262,
2007.
[7] S. Dambreville, R. Sandhu, A. Yezzi, and A. Tannenbaum, “Robust 3d
pose estimation and efficient 2d region-based segmentation from a 3d
shape prior,” in Computer Vision–ECCV 2008. Springer, 2008, pp.
169–182.
[8] C. Reinbacher, M. R¨uther, and H. Bischof, “Pose estimation of known
objects by efficient silhouette matching,” in Proceedings of the 2010
20th International Conference on Pattern Recognition. IEEE Computer
Society, 2010, pp. 1080–1083.
[9] V. Prisacariu and I. Reid, “Pwp3d: Real-time segmentation and tracking
of 3d objects,” International Journal of Computer Vision, pp. 1–20,
2012.
[10] C. Meilhac and C. Nastar, “Robust fitting of 3d cad models to video
streams,” in Image Analysis and Processing. Springer, 1997, pp.
661–668.
[11] E. Rosten and T. Drummond, “Fusing points and lines for high
performance tracking,” in Computer Vision, 2005. ICCV 2005. Tenth
IEEE International Conference on, vol. 2. IEEE, 2005, pp. 1508–1515.
[12] S. Hinterstoisser, S. Benhimane, and N. Navab, “N3m: Natural 3d
markers for real-time object detection and pose estimation,” in Computer
Vision, 2007. ICCV 2007. IEEE 11th International Conference on.
IEEE, 2007, pp. 1–7. [13] A. Del Bue, “Adaptive metric registration of 3d models to non-rigid
image trajectories,” Computer Vision–ECCV 2010, pp. 87–100, 2010.
[14] S. M. Khan, H. Cheng, D. Matthies, and H. Sawhney, “3d model based
vehicle classification in aerial imagery,” in Computer Vision and Pattern
Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010, pp.
1681–1687.
[15] E. Marchand, P. Bouthemy, F. Chaumette, V. Moreau et al., “Robust
real-time visual tracking using a 2d-3d model-based approach,” in IEEE
Int. Conf. on Computer Vision, ICCV’99, vol. 1, 1999, pp. 262–268.
[16] C. Schmaltz, B. Rosenhahn, T. Brox, D. Cremers, J. Weickert,
L. Wietzke, and G. Sommer, “Region-based pose tracking,” in Pattern
Recognition and Image Analysis. Springer, 2007, pp. 56–63.
[17] S. Messelodi, C. Modena, and M. Zanin, “A computer vision system for
the detection and classification of vehicles at urban road intersections,”
Pattern Analysis & Applications, vol. 8, no. 1, pp. 17–31, 2005.
[18] N. Buch, J. Orwell, and S. Velastin, “Urban road user detection and
classification using 3d wire frame models,” Computer Vision, IET, vol. 4,
no. 2, pp. 105–116, 2010.
[19] Z. Zhang, T. Tan, K. Huang, and Y. Wang, “Three-dimensional
deformable-model-based localization and recognition of road vehicles,”
Image Processing, IEEE Transactions on, vol. 21, no. 1, pp. 1–13, 2012.
[20] H. Kollnig and H. Nagel, “3d pose estimation by directly matching
polyhedral models to gray value gradients,” International Journal of
Computer Vision, vol. 23, no. 3, pp. 283–302, 1997.
[21] T. Tan and K. Baker, “Efficient image gradient based vehicle
localization,” Image Processing, IEEE Transactions on, vol. 9, no. 8,
pp. 1343–1356, 2000.
[22] D. Roller, K. Daniilidis, and H. Nagel, “Model-based object tracking in
monocular image sequences of road traffic scenes,” International Journal
of Computer Vision, vol. 10, no. 3, pp. 257–281, 1993.
[23] M. Haag and H. Nagel, “Combination of edge element and optical
flow estimates for 3d-model-based vehicle tracking in traffic image
sequences,” International Journal of Computer Vision, vol. 35, no. 3,
pp. 295–319, 1999.
[24] J. Lou, T. Tan, W. Hu, H. Yang, and S. Maybank, “3-d model-based
vehicle tracking,” Image Processing, IEEE Transactions on, vol. 14,
no. 10, pp. 1561–1569, 2005.
[25] M. J. Leotta and J. L. Mundy, “Vehicle surveillance with a generic,
adaptive, 3d vehicle model,” Pattern Analysis and Machine Intelligence,
IEEE Transactions on, vol. 33, no. 7, pp. 1457–1469, 2011.
[26] B. Yan, S. Wang, Y. Chen, and X. Ding, “Deformable 3-d model
based vehicle matching with weighted hausdorff and eda in traffic
surveillance,” in Image Analysis and Signal Processing (IASP), 2010
International Conference on. IEEE, 2010, pp. 22–27.
[27] M. Hodlmoser, B. Micusik, M. Y. Liu, M. Pollefeys, and M. Kampel,
“Classification and pose estimation of vehicles in videos by 3d modeling
within discrete-continuous optimization,” in 3D Imaging, Modeling,
Processing, Visualization and Transmission (3DIMPVT), 2012 Second
International Conference on. IEEE, 2012, pp. 198–205.
[28] S. Gupte, O. Masoud, R. Martin, and N. Papanikolopoulos, “Detection
and classification of vehicles,” Intelligent Transportation Systems, IEEE
Transactions on, vol. 3, no. 1, pp. 37–47, 2002.
[29] Y. Tsin, Y. Genc, and V. Ramesh, “Explicit 3d modeling for vehicle
monitoring in non-overlapping cameras,” in Advanced Video and
Signal Based Surveillance, 2009. AVSS’09. Sixth IEEE International
Conference on. IEEE, 2009, pp. 110–115.
[30] Q. Liu, J. Lou, W. Hu, and T. Tan, “Pose evaluation based on
bayesian classification error,” in In Proc. of 14th British Machine Vision
Conference, 2003, pp. 409–418.
[31] B. Johansson, J. Wiklund, P. Forss´en, and G. Granlund, “Combining
shadow detection and simulation for estimation of vehicle size and
position,” Pattern Recognition Letters, vol. 30, no. 8, pp. 751–759, 2009.
[32] S. Jayawardena, M. Hutter, and N. Brewer, “Featureless 2d–3d pose
estimation by minimising an illumination-invariant loss,” in Image
and Vision Computing New Zealand (IVCNZ), 2010 25th International
Conference of. IEEE, 2010, pp. 1–8. [33] T. Hou, S. Wang, and H. Qin, “Vehicle matching and recognition under
large variations of pose and illumination,” in Computer Vision and
Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE
Computer Society Conference on. IEEE, 2009, pp. 24–29.
[34] K. Brisdon, “Hypothesis verification using iconic matching.” Ph.D.
dissertation, University of Reading, 1990.
[35] A. Pece and A. Worrall, “Tracking without feature detection,” in
Proc. IEEE Int. Workshop Performance Evaluation of Tracking and
Surveillance. Citeseer, 2000, pp. 29–37.
[36] M. Hodlmoser, B. Micusik, M. Pollefeys, M.-Y. Liu, and M. Kampel,
“Model-based vehicle pose estimation and tracking in videos using
random forests,” in 3DTV-Conference, 2013 International Conference
on. IEEE, 2013, pp. 430–437.
@article{"International Journal of Information, Control and Computer Sciences:72972", author = "Yuan Zheng", title = "A Practical and Efficient Evaluation Function for 3D Model Based Vehicle Matching", abstract = "3D model-based vehicle matching provides a new way
for vehicle recognition, localization and tracking. Its key is to
construct an evaluation function, also called fitness function, to
measure the degree of vehicle matching. The existing fitness functions
often poorly perform when the clutter and occlusion exist in traffic
scenarios. In this paper, we present a practical and efficient fitness
function. Unlike the existing evaluation functions, the proposed
fitness function is to study the vehicle matching problem from
both local and global perspectives, which exploits the pixel gradient
information as well as the silhouette information. In view of the
discrepancy between 3D vehicle model and real vehicle, a weighting
strategy is introduced to differently treat the fitting of the model’s
wireframes. Additionally, a normalization operation for the model’s
projection is performed to improve the accuracy of the matching.
Experimental results on real traffic videos reveal that the proposed
fitness function is efficient and robust to the cluttered background
and partial occlusion.", keywords = "3D-2D matching, fitness function, 3D vehicle model,
local image gradient, silhouette information.", volume = "10", number = "6", pages = "983-7", }
