Dunne, Peter John (2012) Tracking moving objects in surveillance video. Doctoral thesis, University of Central Lancashire.
The thesis looks at approaches to the detection and tracking of potential objects of interest in surveillance video. The aim was to investigate and develop methods that might be suitable for eventual application through embedded software, running on a fixed-point processor, in analytics capable cameras.
The work considers common approaches to object detection and representation, seeking out those that offer the necessary computational economy and the potential to be able to cope with constraints such as low frame rate due to possible limited processor time, or weak chromatic content that can occur in some typical surveillance contexts.
The aim is for probabilistic tracking of objects rather than simple concatenation of frame by frame detections. This involves using recursive Bayesian estimation. The particle filter is a technique for implementing such a recursion and so it is examined in the context of both single target and combined multi-target tracking.
A detailed examination of the operation of the single target tracking particle filter shows that objects can be tracked successfully using a relatively simple structured grey-scale histogram representation. It is shown that basic components of the particle filter can be simplified without loss in tracking quality. An analysis brings out the relationships between commonly used target representation distance measures and shows that in the context of the particle filter there is little to choose between them. With the correct choice of parameters, the simplest and computationally economic distance measure performs well. The work shows how to make that correct choice. Similarly, it is shown that a simple measurement likelihood function can be used in place of the more ubiquitous Gaussian.
The important step of target state estimation is examined. The standard weighted mean approach is rejected, a recently proposed maximum a posteriori approach is shown to be not suitable in the context of the work, and a practical alternative is developed.
Two methods are presented for tracker initialization. One of them is a simplification of an existing published method, the other is a novel approach. The aim is to detect trackable objects as they enter the scene, extract trackable features, then actively follow those features through subsequent frames. The multi-target tracking problem is then posed as one of management of multiple independent trackers.
|Item Type:||Thesis (Doctoral)|
|Additional Information:||Publications P.Dunne and B. Matuszewski. Choice of similarity measure, likelihood function and parameters for histogram based particle filter tracking in CCTV grey scale video. Image and Vision Computing, 29(2-3):178-189, 2011 P.Dunne and B. Matuszewski. Histogram-based detection of moving objects for tracker initialization in surveillance video. International Journal of Grid and Distributed Computing, 4(3):71-78, 2011|
|Uncontrolled Keywords (separate with ;):||Tracking;Surveillance video;Bayesian;Particle Filter; Likelihood; Distance measure|
|Subjects:||Engineering > Civil engineering|
|Schools:||Faculty of Science and Technology > School of Physical Sciences and Computing|
|Deposited By:||Hayley Gayle Moran|
|Deposited On:||02 May 2013 09:20|
|Last Modified:||10 Feb 2017 12:42|
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