Abstract
Detection counting as well as gathering features to perform analysis of behavior of natural scene is one of the complex processes to be design. The current work focuses not only to detect and count the multiple moving objects but also to understand the crowd behavior as well as exponentially reduce the issues of inter-object occlusion. The image frame sequence is considered as input for the proposed framework. Unscented Kalman filter is used for understanding the behavior of the scene as well as for increasing the detection accuracy and reducing the false positives. Designed on Matlab environment, the result shows highly accurate detection rate.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Josef S, Russell BC, Alexei A, Zisserman A, William T (2005) Discovering objects and their location in images. Proceedings ICCV’05, Beijing, China, 370–377 2005
Ng AY, Jordan MI (2001) On discriminative versus generative classifiers: a comparison of logistic regression and naive bayes. In: Proceedings NIPS’01, Vancouver, British Columbia, Canada, 841–848 2001
Ferrari V, Fevrier L, Jurie F, Schmid C (2008) Group of adjacent contour segment for object detection. PAMI 30(1):30–51
Merad D, Aziz KE, Thome N (2010) Fast people counting using head detection from skeleton graph. In: 2010 Seventh IEEE international conference on advanced video and signal based surveillance
Conte D, Foggia P, Percannella G, Tufano F (2010) A method for counting people in crowded scenes, EURASIP Journal on Advanced Signal Processing, 2010
Burkert F, Schmidt F, Butenuth M, Hinz S (2010) People tracking and trajectory interpretation in aerial image sequences. IAPRS 38:209–214
Conte D, Foggia P, Percannella G, Tufano F, Vento M (2010) Counting moving people in videos by salient points detection. In: International conference on pattern recognition, 2010
Widhalm P, Brandle N (2010) Learning Major Pedestrian Flows in Crowded Scenes. In: International conference on pattern recognition, 2010
Dehghan A, Idrees H, Zamir AR, Shah M (2011) Keynote: automatic detection and tracking of pedestrians in videos with various crowd densities. Springer, Berlin
Spampinato C, Faro A, Palazzo S (2011) Event detection in crowds of people by integrating chaos and lagrangian particle dynamics. In: Proceedings 3rd international conference on information and multimedia technology (ICIMT 2011), Dubai, UAE, 28–30 Dec 2011
Suhr JK (2011) Moving object detection for static and Pan-Tilt-Zoom cameras in intelligent visual surveillance, A doctorial thesis, Graduate School of Yonsei University, 2011
Lei PR, Su J, Peng WC, Han WY, Chang CP (2011) A framework of moving behavior modeling in the maritime surveillance, Journal of Chung Cheng Institute of Technology, 2011
Sirmacek B, Reinartz P (2011) Kalman filter based feature analysis for tracking people from airborne images. In: ISPRS workshop high-resolution earth imaging for geospatial information, Hannover, Germany, Jun 2011
Cui Y, Zeng Z, Cui W, Fu B (2012) Moving object detection based frame difference and graph cuts. J Comput Inf Syst 8(1):21–29
Chen CH, Chen TY, Wang DJ, Chen TJ (2012) A cost-effective people-counter for a crowd of moving people based on two-stage segmentation. J Inform Hiding Multimedia Signal Process 3(1):12–25. ISSN 2073-4212
Gowsikhaa D, Manjunath AS (2012) Suspicious human activity detection from surveillance videos, (IJIDCS). Int J Internet Distrib Comput Syst 2(2):141–149
Rosswog J, Ghose K (2012) Detecting and tracking coordinated groups in dense, systematically moving, crowds, SIAM-2012
Arif M, Saqib M, Basalamah S, Naeem A (2012) Counting of moving people in the video using neural network system. Life Sci J 9(3):1384–1392
Pushpa D, Sheshadri HS (2012) Precise multiple object identification and tracking using efficient visual attributes in dense crowded scene with regions of rational movement. IJCSI Int J Comput Sci 9(2)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Pushpa, D., Sheshadri, H.S. (2014). Semantic Analysis of Precise Detection Rate in Multi-Object Mobility on Natural Scene Using Kalman Filter. In: Sridhar, V., Sheshadri, H., Padma, M. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 248. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1157-0_23
Download citation
DOI: https://doi.org/10.1007/978-81-322-1157-0_23
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1156-3
Online ISBN: 978-81-322-1157-0
eBook Packages: EngineeringEngineering (R0)