DocumentCode :
2653673
Title :
Model Update Particle Filter for Multiple Objects Detection and Tracking
Author :
Zhao, Yun Ji ; Pei, Hai Long
Author_Institution :
Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China
fYear :
2011
fDate :
22-23 Oct. 2011
Firstpage :
9
Lastpage :
12
Abstract :
Multiple objects tracking is a challenging task. This article presents an algorithm which can detect and track multiple objects, and update target model automatically. The contributions of this paper as follow: Firstly, we use color histogram(HC) and histogram of orientated gradients(HOG) to represent the objects, model update is realized under the frame of kalman filter and gaussian model, secondly we use Gaussian Mixture Model(GMM) and Bhattacharyya distance to detect object appearance. Particle filter with combined features and model update mechanism can improve tracking effects. Experiments on video sequences demonstrate that multiple objects tracking based on improved algorithm have good performance.
Keywords :
Gaussian processes; image colour analysis; object detection; particle filtering (numerical methods); Gaussian mixture model; Gaussian model; color histogram; histogram of orientated gradients; kalman filter; object detection; object tracking; particle filter; video sequences; Adaptation models; Computational modeling; Feature extraction; Histograms; Image color analysis; Object detection; Target tracking; Gaussian Mixture Model; Particle filter; color histogram; histogram of orientated gradients;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence Information Processing and Trusted Computing (IPTC), 2011 2nd International Symposium on
Conference_Location :
Hubei
Print_ISBN :
978-1-4577-1130-5
Type :
conf
DOI :
10.1109/IPTC.2011.10
Filename :
6103524
Link To Document :
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