an efficient real time people counting system based on identification and tracking using surveillance camera
VASANTHAPRIYA.M,PALANIVEL.N,SEETHARAMAN.K
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:4 Issue:3 Year: 29 March,2016 Pages:495-501
Abstract
A Framework for track human motion in an enclosed atmosphere from sequences of monocular gray scale pictures that are obtained from mounted cameras. The detection of objects that are moving uses background subtraction algorithm which is working based on Gaussian mixture models. Variable Gaussian models square measure applied to seek out the most likely matches of human movements between successive frames taken by cameras mounted in varied locations. The modified Kalman filters is used for tracking objects in each frame, and determine the possibility of each detection is being assigned to each track. An important aspect of this project is Track maintenance.
Kewords
Tracking, Background Subtraction, Segmentation, Coarse detection, counting
Reference
[1] NickC.Tang, Yen-Yu Lin, Ming Fang Weng and Hong-YuanMarkLiao,“Cross-Camera knowledge Transfer for Multiviewpeople counting”, IEEE Trans on Image Processing, Vol.24, No:1. [2] Y.-L. Hou and G. K. H. Pang, (2011). “People counting and human detection in a challenging situation,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans,vol. 41, no. 1, pp. 24–33, Jan. [3] X. Zhao, E. Delleandrea, and L. Chen, (2009). “A people counting system based on face detection and tracking in a video,” in Proc. IEEE Int. Conf. AVSS, pp. 67–72. [4] Xiaowei Zhou, Can Yang, and Weichuan Yu, (2013). “Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation” IEEE Trans. Vol. 35, No. 3. [5] Shi-qi Xing, Yong-zhen Li, Da-hai Dai, Xue-Song Wang, (2012). “Three-Dimensional Reconstruction of ManMade Objects Using Polarimetric Tomographic SAR”, IEEE Trans. Vol. 51, Issue: 6 Journal on Geoscience and Remote Sensing. [6] Liyuan Li, Weimin Huang, Irene Yu-HuaGu, and Qi Tian, (2004). “Statistical Modelling of Complex Backgrounds for Foreground Object Detection” IEEE Trans on Image Processing, Vol. 13, NO. 11. [7] Weiming Hu, Xue Zhou, Wei Li, WenhanLuo, Xiaoqin Zhang, and Stephen Maybank, (2013). “Active ContourBased Visual Tracking by Integrating Colors, Shapes, and Motions Image Processing”, IEEE Trans. on Image Processing,Vol.22,Issue: 5. [8] Hsu-Yung Cheng, Chih-Chia Weng, Yi-Ying Chen, (2012). “Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks”, IEEE Trans on Image Processing, Vol.21, Issue: 4. [9] Pushe Zhao, Hongbo Zhu, He Li, and Tadashi Shibata, (2013). “A Directional-Edge-Based Real-Time Object Tracking System Employing Multiple Candidate-Location Generation” IEEE Trans. vol. 23, no. 3, Mar. [10] Wang. H andSuter. D, (2006). “A Novel Robust Statistical Method for Background Initialization and Visual Surveillance” Proc. Asian Conf. Computer Vision. [12] Michael.BHolte, Cuong Tran, Mohan. M Trivedi, Fellow, and Thomas B. Moeslund, Member, (2012). “Human Pose Estimation and Activity Recognition From Multi-View Videos: Comparative Explorations of Recent Developments” Vol. 6, No. 5. [13] Nair.V and Clark.J, (2004). “An Unsupervised, Online Learning Framework for Moving Object Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 317 -324. [14] Abhijitmahalanobis, Robert muise and Lockheed Martin, (2007). “Object Specific Image Reconstruction using a Compressive Sensing Architecture for Application in Surveillance System” IEEE TRANSACTION. [15] Brox. T, Bruhn. V and Weickert.J, (2006).“Variational Motion Segmentation with Level Sets,” Proc. European Conf. Computer Vision.
