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Video content analysis for automated detection and tracking of humans in CCTV surveillance applications
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented
Object segmentation from low depth of field images and video sequences
This thesis addresses the problem of autonomous object segmentation. To do so
the proposed segementation method uses some prior information, namely that the
image to be segmented will have a low depth of field and that the object of interest
will be more in focus than the background. To differentiate the object from the
background scene, a multiscale wavelet based assessment is proposed. The focus
assessment is used to generate a focus intensity map, and a sparse fields level set
implementation of active contours is used to segment the object of interest. The
initial contour is generated using a grid based technique.
The method is extended to segment low depth of field video sequences with
each successive initialisation for the active contours generated from the binary dilation
of the previous frame's segmentation. Experimental results show good segmentations
can be achieved with a variety of different images, video sequences, and
objects, with no user interaction or input.
The method is applied to two different areas. In the first the segmentations
are used to automatically generate trimaps for use with matting algorithms. In the
second, the method is used as part of a shape from silhouettes 3D object reconstruction
system, replacing the need for a constrained background when generating
silhouettes. In addition, not using a thresholding to perform the silhouette segmentation
allows for objects with dark components or areas to be segmented accurately.
Some examples of 3D models generated using silhouettes are shown
Long Range Automated Persistent Surveillance
This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging.
field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped cameraâs field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales.
Size preserving tracking automatically adjusts the cameraâs zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the targetâs 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels.
Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images
Filtering of image sequences: on line edge detection and motion reconstruction
L'argomento della Tesi riguarda lĂelaborazione di sequenze di immagini, relative ad una
scena in cui uno o piË oggetti (possibilmente deformabili) si muovono e acquisite da un
opportuno strumento di misura. A causa del processo di misura, le immagini sono corrotte da
un livello di degradazione. Si riporta la formalizzazione matematica dellĂinsieme delle
immagini considerate, dellĂinsieme dei moti ammissibili e della degradazione introdotta dallo
strumento di misura. Ogni immagine della sequenza acquisita ha una relazione con tutte le
altre, stabilita dalla legge del moto della scena. LĂidea proposta in questa Tesi Ă quella di
sfruttare questa relazione tra le diverse immagini della sequenza per ricostruire grandezze di
interesse che caratterizzano la scena.
Nel caso in cui si conosce il moto, lĂinteresse Ă quello di ricostruire i contorni dellĂimmagine
iniziale (che poi possono essere propagati attraverso la stessa legge del moto, in modo da
ricostruire i contorni della generica immagine appartenente alla sequenza in esame), stimando
lĂampiezza e del salto del livello di grigio e la relativa localizzazione.
Nel caso duale si suppone invece di conoscere la disposizione dei contorni nellĂimmagine
iniziale e di avere un modello stocastico che descriva il moto; lĂobiettivo Ă quindi stimare i
parametri che caratterizzano tale modello.
Infine, si presentano i risultati dellĂapplicazione delle due metodologie succitate a dati reali
ottenuti in ambito biomedicale da uno strumento denominato pupillometro. Tali risultati sono
di elevato interesse nellĂottica di utilizzare il suddetto strumento a fini diagnostici
Connected Attribute Filtering Based on Contour Smoothness
A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform
Advancements and Breakthroughs in Ultrasound Imaging
Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world
Adaptive object segmentation and tracking
Efficient tracking of deformable objects moving with variable velocities is an important current research problem. In this thesis a robust tracking model is proposed for the automatic detection, recognition and tracking of target objects which are subject to variable orientations and velocities and are viewed under variable ambient lighting conditions. The tracking model can be applied to efficiently track fast moving vehicles and other objects in various complex scenarios. The tracking model is evaluated on both colour visible band and infra-red band video sequences acquired from the air by the Sussex police helicopter and other collaborators. The observations made validate the improved performance of the model over existing methods.
The thesis is divided in three major sections. The first section details the development of an enhanced active contour for object segmentation. The second section describes an implementation of a global active contour orientation model. The third section describes the tracking model and assesses it performance on the aerial video sequences.
In the first part of the thesis an enhanced active contour snake model using the difference of Gaussian (DoG) filter is reported and discussed in detail. An acquisition method based on the enhanced active contour method developed that can assist the proposed tracking system is tested. The active contour model is further enhanced by the use of a disambiguation framework designed to assist multiple object segmentation which is used to demonstrate that the enhanced active contour model can be used for robust multiple object segmentation and tracking. The active contour model developed not only facilitates the efficient update of the tracking filter but also decreases the latency involved in tracking targets in real-time. As far as computational effort is concerned, the active contour model presented improves the computational cost by 85% compared to existing active contour models.
The second part of the thesis introduces the global active contour orientation (GACO) technique for statistical measurement of contoured object orientation. It is an overall object orientation measurement method which uses the proposed active contour model along with statistical measurement techniques. The use of the GACO technique, incorporating the active contour model, to measure object orientation angle is discussed in detail. A real-time door surveillance application based on the GACO technique is developed and evaluated on the i-LIDS door surveillance dataset provided by the UK Home Office. The performance results demonstrate the use of GACO to evaluate the door surveillance dataset gives a success rate of 92%.
Finally, a combined approach involving the proposed active contour model and an optimal trade-off maximum average correlation height (OT-MACH) filter for tracking is presented. The implementation of methods for controlling the area of support of the OT-MACH filter is discussed in detail. The proposed active contour method as the area of support for the OT-MACH filter is shown to significantly improve the performance of the OT-MACH filter's ability to track vehicles moving within highly cluttered visible and infra-red band video sequence
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