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Distributed Tracking in Smart Camera Networks
, M.Sc. Thesis Sharif University of Technology ; Hossein Khalaj, Babak (Supervisor)
Abstract
Human tracking is an essential step in many computer vision-based applications. As single view tracking may not be sufficiently robust and accurate, tracking based on multiple cameras has been widely considered in recent years. This thesis presents a distributed human tracking method in a smart camera network and introduces a particle filter design based on Histogram of Oriented Gradients (HOG) and color histogram. The proposed adaptive motion model also estimates the target speed from the history of its latest displacement and improves the robustness of the tracker by decreasing the probability of missing targets. In addition, a distributed data fusion method is proposed which fuses the...
Improving the Performance of Distributed Fusion for PHD Filter in Multi-Object Tracking
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
The Gaussian mixture (cardinalized) probability hypothesis density (GM-(C)PHD) filter is a closed form approximation of multi-target Bayes filter which can overcome most of multi-target tracking problems. Limited field of view, decreasing cost of cameras and its advances induce us to use large-scale camera networks. Increasing the size of camera networks make centralized networks practically inefficient. On the other hand, scalability, simplicity and low data transmission cost has made distributed networks a good replacement for centralized networks. However, data fusion in distributed network is sub-optimal due to unavailable cross-correlation.Among data fusion algorithms which deal with...
Multiple human tracking using PHD filter in distributed camera network
, Article Proceedings of the 4th International Conference on Computer and Knowledge Engineering, ICCKE 2014 ; 2014 , pp. 569-574 ; ISBN: 9781479954865 ; Jamzad, M ; Sharif University of Technology
Abstract
The Gaussian mixture probability hypothesis density (GM-PHD) filter is a closed form approximation of the multi-target Bayes filter which can overcome most multitarget tracking problems. Limited field of view, decreasing cost of cameras, and advances of using multi-camera induce us to use large-scale camera networks. In this paper, a multihuman tracking framework using the PHD filter in a distributed camera network is proposed. Each camera tracks objects locally with PHD filter and a track-after-detect scheme and its estimates of targets are sent to neighboring nodes. Then each camera fuses its local estimates with it's neighbors. The proposed method is evaluated on the public PETS2009...
Distibuted human tracking in smart camera networks by adaptive particle filtering and data fusion
, Article 2012 6th International Conference on Distributed Smart Cameras, ICDSC 2012, 30 October 2012 through 2 November 2012 ; November , 2012 ; 9781450317726 (ISBN) ; Khalaj, B. H ; Sharif University of Technology
2012
Abstract
Human tracking is an essential step in many computer vision-based applications. As single view tracking may not be sufficiently robust and accurate, tracking based on multiple cameras has been widely considered in recent years. This paper presents a distributed human tracking method in a smart camera network and introduces a particle filter design based on Histogram of Oriented Gradients (HOG) and color histogram. The proposed adaptive motion model also estimates the target speed from the history of its latest displacement and improves the robustness of the tracker by decreasing the probability of missing targets. In addition, a distributed data fusion method is proposed which fuses the...