Abstract
Multi object tracking in aerial image sequences is a topic of utmost importance in the field of computer vision for both military and civilian applications. In order to extract valid information about moving targets, it is required to detect and track these targets precisely in the input image sequences. Occlusion is one of the prominent problem areas that hinder efficient object tracking. Spatial reasoning literature fails to distinguish various analyses that are prominent to computer vision. However, recognizing valid occlusion states and mining their transition sequences help in analyzing the pose and motion of multiple interacting objects in the scene. In this paper, we propose an enhancement incorporating occlusion in the existing COCOA framework for tracking in aerial image sequence. The contribution of the paper is the novel idea of extracting occlusion cues as a pre-processing step to aid the tracker. We describe approaches to extract occlusion information in the scene and use it as a cue for efficient tracking.
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References
Ali S, Shah M (2006) COCOA—tracking in aerial imagery. SPIE Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications, Orlando
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Computer Vision 60(2):91–110
Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: The proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2
Reilly V, Idrees H, Shah M (2010) Detection and tracking of large number of targets in wide area surveillance. In: The proceedings of 11th European Conference on Computer Vision, LNCS, vol 6313. pp 186–199, Springer
Senior A, Hampapur A, Tian Y, Brown L, Pankanti S, Bolle R (2006) Appearance models for occlusion handling. In: 2nd International Workshop on Performance Evaluation of Tracking and Surveillance Systems, Science Direct, Elsevier, Image and Vision Computing, vol 24. pp 1233–1243
Guha P, Mukerjee A, Venkatesh KS (2011) OCS-14: you can get occluded in fourteen ways. In: Proceedings of 22nd International Joint Conference on Artificial Intelligence, pp 1665–1675
Guha P, Mukerjee A, Venkatesh KS (2006) Appearance based multiple agent tracking under complex occlusions. PRICAI 2006: Trends in Artificial Intelligence LNCS, vol 4099. Springer, Heidelberg, pp 593–602
Galton A (1998) Modes of overlap. J Vis Lang Comput 9(1):61–79
Kohler C (2002) The occlusion calculus. In: Workshop on Cognitive Vision, Zurich, Switzerland
Cao H, Mamoulis N, Cheung DW (2005) Mining frequent spatio temporal sequence patterns. In: 5th IEEE International Conference on Data Mining, pp 82–89
Acknowledgments
This work is funded by ER & IPR, DRDO ref no: ERIP/ER/1104561/M/01/1353, India.
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Malagi, V.P., Gayatri, V.V., Rangarajan, K., Ramesh Babu, D.R. (2013). Enhancing COCOA Framework for Tracking Multiple Objects in the Presence of Occlusion in Aerial Image Sequences. In: Swamy, P., Guru, D. (eds) Multimedia Processing, Communication and Computing Applications. Lecture Notes in Electrical Engineering, vol 213. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1143-3_18
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DOI: https://doi.org/10.1007/978-81-322-1143-3_18
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