Automatic crowd analysis from very high resolution satellite images

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Volumn 38, Issue 3W22, 2011, Pages 221-226

  Sirmacek, Beril ^a   Reinartz, Peter ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Crowd detection; DEM; Local features; Probability theory; Road extraction; Shadow extraction; Very high resolution satellite images

Indexed keywords

CAMERAS; EXTRACTION; HUMAN RESOURCE MANAGEMENT; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE SEGMENTATION; PROBABILITY DENSITY FUNCTION; SATELLITE IMAGERY; SATELLITES; STEREO IMAGE PROCESSING; SURVEYING;

CROWD DETECTION; DEM; LOCAL FEATURE; PROBABILITY THEORY; ROAD EXTRACTION; SHADOW EXTRACTIONS; VERY HIGH RESOLUTION SATELLITE IMAGES;

FEATURE EXTRACTION;

EID: 84923874183     PISSN: 16821750     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Arandjelovic, O., Sep. 2008. Crowd detection from still images. British Machine Vision Conference (BMVC'08).
2. Burkert, F., Schmidt, F., Butenuth, M. and Hinz, S., Sep. 2010. People tracking and trajectory interpretation in aerial image sequences. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (IAPRS), Commission III (Part A) XXXVIII, pp. 209-214.
3. d'Angelo, P., Schwind, P., Krauss, T., Barner, F. and Reinartz, P., 2009. Automated dsm based georeferencing of cartosat-1 stereo scenes. In Proceedings of International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences.
4. Davies, A., Yin, J. and Velastin, S., 1995. Crowd monitoring using image processing. IEEE Electronic and Communications Engineering Journal 7 (1), pp. 37-47.
5. Ge, W. and Collins, R., 2009. Marked point process for crowd counting. IEEE Computer Vision and Pattern Recognition Conference (CVPR'09) pp. 2913-2920.
6. Ge, W. and Collins, R., 2010. Crowd detection with a multiview sampler. European Conference on Computer Vision (ECCV'10).
7. Hinz, S., 2009. Density and motion estimation of people in crowded environments based on aerial image sequences. ISPRS Hannover Workshop on High-Resolution Earth Imaging for Geospatial Information.
8. Lin, S., Chen, J. and Chao, H., Nov. 2001. Estimation of number of people in crowded scenes using perspective transformation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 31 (6), pp. 645-654.
9. Otsu, N., 2009. A threshold selection method from gray-level histograms. IEEE Transactions on System, Man, and Cybernetics 9 (1), pp. 62-66.
10. Regazzoni, C. and Tesei, A., 1994. Local density evaluation and tracking of multiple objects from complex image sequences. Proceedings of 20th International Conference on Industrial Electronics, Control and Instrumentation (IECON) 2, pp. 744-748.
11. Regazzoni, C. and Tesei, A., 1996. Distributed data fusion for real time crowding estimation. Signal Processing 53, pp. 47-63.
12. Rosten, E., Porter, R. and Drummond, T., Nov. 2010. Faster and better: A machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Learning 32 (1), pp. 105-119.
13. Silverman, B., 1986. Density estimation for statistics and data analysis. 1st Edition.
14. Sirmacek, B. and Reinartz, P., 2011a. Automatic crowd analysis from airborne images. 5th International Conference on Recent Advances in Space Technologies RAST 2011, Istanbul, Turkey.
15. Sirmacek, B. and Reinartz, P., 2011b. Kalman filter based feature analysis for tracking people from airborne images. ISPRS Workshop HighResolution Earth Imaging for Geospatial Information, Hannover, Germany.
16. Sirmacek, B. and Unsalan, C., 2010. A probabilistic framework to detect buildings in aerial and satellite images. IEEE Transactions on Geoscience and Remote Sensing.