Vehicle Detection from Aerial Images Using Local Shape Information
Abstract
Detection and extraction of vehicle objects in high resolution satellite imagery are required in many transportation applications. This paper presents an approach to automatic vehicle detection from aerial images. The initial extraction of candidate vehicle is based on Mean-shift algorithm with symmetric character of blob-like car structure. By fusing the density and the symmetry, the method can remove the ambiguous blobs and reduce the cost of the detected ROI processing in the subsequent stage. To verify the blob as a vehicle, log-polar shape descriptor is used for measuring similarity. The edge strengths are obtained and represented as its spatial histogram by the orientation and distance from the center of blob. The proposed algorithm is able to successfully detect the vehicle and very useful for the traffic surveillance system.
Keywords
Vehicle detection Aerial imagery Traffic monitoring Mean shift Shape description symmetryReferences
- 1.Punvatavungkour, S., Shibasaki, R.: Three Line Scaner Imagery and On-street Packed Vehicle Detection. Int’l Archives of Photogrammetry Remote Sensing and Spatial Information Sciences 35(3), 355–359 (2004)Google Scholar
- 2.Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int’l J. Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
- 3.Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 4.Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
- 5.Zhao, T., Nevatia, R.: Car Detection in Low Resolution Aerial Images. Image and Vision Computing 21, 693–703 (2003)CrossRefGoogle Scholar
- 6.Chellappa, R., Burlina, P., Davis, L.S., Rosenfeld, A.: SAR/EO Vehicular Activity Analysis System Guided by Temporal and Contextual Information. In: Proc. 1994 ARPA Image Understanding Workshop, pp. 615–620 (1998)Google Scholar
- 7.Moon, H., Chellappa, R., Rosenfeld, A.: Performance Analysis of a Simple Vehicle Detection Algorithm. Image and Vision Computing 20(1), 1–13 (2002)CrossRefGoogle Scholar
- 8.Hinz, S.: Integrating Local and Global Features for Vehicle Detection in High Resolution Aerial Imagery. Photogrammetry Remote Sensing Spatial Information 34(3W/8), 119–124 (2003)Google Scholar
- 9.Ruskone, R., Guigues, L., Airault, S., Jamet, O.: Vehicle Detection on Aerial Images: A Structural Approach. In: Proc. Int’l Conf. Pattern Recognition, pp. 900–904 (1996)Google Scholar
- 10.Fukunaga, K., Hostetler, L.D.: The Estimation of the Gradient of a Density Function with Applications in Pattern Recognition. IEEE Trans. Information Theory 21(1), 32–40 (1975)MathSciNetCrossRefMATHGoogle Scholar
- 11.Comaniciu, D., Meer, P.: Robust Analysis of Feature Spaces: Color image Segmentation. In: IEEE Conf. Computer Vision and Pattern Recognition, pp. 750–755 (1997)Google Scholar
- 12.Kovesi, P.: Symmetry and Asymmetry from Local Phase. In: 10th Australian Joint Conf. on Artificial Intelligence, pp. 185–190 (1997)Google Scholar
- 13.Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition using Shape Contexts. IEEE trans. Pattern Analysis and Machine Intelligence 24(24), 509–522 (2002)CrossRefGoogle Scholar