1,830 research outputs found
Adaptive visual sampling
PhDVarious visual tasks may be analysed in the context of sampling from the visual field. In visual
psychophysics, human visual sampling strategies have often been shown at a high-level to
be driven by various information and resource related factors such as the limited capacity of
the human cognitive system, the quality of information gathered, its relevance in context and
the associated efficiency of recovering it. At a lower-level, we interpret many computer vision
tasks to be rooted in similar notions of contextually-relevant, dynamic sampling strategies
which are geared towards the filtering of pixel samples to perform reliable object association. In
the context of object tracking, the reliability of such endeavours is fundamentally rooted in the
continuing relevance of object models used for such filtering, a requirement complicated by realworld
conditions such as dynamic lighting that inconveniently and frequently cause their rapid
obsolescence. In the context of recognition, performance can be hindered by the lack of learned
context-dependent strategies that satisfactorily filter out samples that are irrelevant or blunt the
potency of models used for discrimination. In this thesis we interpret the problems of visual
tracking and recognition in terms of dynamic spatial and featural sampling strategies and, in this
vein, present three frameworks that build on previous methods to provide a more flexible and
effective approach.
Firstly, we propose an adaptive spatial sampling strategy framework to maintain statistical object
models for real-time robust tracking under changing lighting conditions. We employ colour
features in experiments to demonstrate its effectiveness. The framework consists of five parts:
(a) Gaussian mixture models for semi-parametric modelling of the colour distributions of multicolour
objects; (b) a constructive algorithm that uses cross-validation for automatically determining
the number of components for a Gaussian mixture given a sample set of object colours; (c) a
sampling strategy for performing fast tracking using colour models; (d) a Bayesian formulation
enabling models of object and the environment to be employed together in filtering samples by
discrimination; and (e) a selectively-adaptive mechanism to enable colour models to cope with
changing conditions and permit more robust tracking.
Secondly, we extend the concept to an adaptive spatial and featural sampling strategy to deal
with very difficult conditions such as small target objects in cluttered environments undergoing
severe lighting fluctuations and extreme occlusions. This builds on previous work on dynamic
feature selection during tracking by reducing redundancy in features selected at each stage as
well as more naturally balancing short-term and long-term evidence, the latter to facilitate model
rigidity under sharp, temporary changes such as occlusion whilst permitting model flexibility
under slower, long-term changes such as varying lighting conditions. This framework consists of
two parts: (a) Attribute-based Feature Ranking (AFR) which combines two attribute measures;
discriminability and independence to other features; and (b) Multiple Selectively-adaptive Feature
Models (MSFM) which involves maintaining a dynamic feature reference of target object
appearance. We call this framework Adaptive Multi-feature Association (AMA). Finally, we present an adaptive spatial and featural sampling strategy that extends established
Local Binary Pattern (LBP) methods and overcomes many severe limitations of the traditional
approach such as limited spatial support, restricted sample sets and ad hoc joint and disjoint statistical
distributions that may fail to capture important structure. Our framework enables more
compact, descriptive LBP type models to be constructed which may be employed in conjunction
with many existing LBP techniques to improve their performance without modification. The
framework consists of two parts: (a) a new LBP-type model known as Multiscale Selected Local
Binary Features (MSLBF); and (b) a novel binary feature selection algorithm called Binary Histogram
Intersection Minimisation (BHIM) which is shown to be more powerful than established
methods used for binary feature selection such as Conditional Mutual Information Maximisation
(CMIM) and AdaBoost
Gaussian mixture model classifiers for detection and tracking in UAV video streams.
Masters Degree. University of KwaZulu-Natal, Durban.Manual visual surveillance systems are subject to a high degree of human-error and operator fatigue. The automation of such systems often employs detectors, trackers and classifiers as fundamental building blocks. Detection, tracking and classification are especially useful and challenging in Unmanned Aerial Vehicle (UAV) based surveillance systems. Previous solutions have addressed challenges via complex classification methods. This dissertation proposes less complex Gaussian Mixture Model (GMM) based classifiers that can simplify the process; where data is represented as a reduced set of model parameters, and classification is performed in the low dimensionality parameter-space. The specification and adoption of GMM based classifiers on the UAV visual tracking feature space formed the principal contribution of the work. This methodology can be generalised to other feature spaces.
This dissertation presents two main contributions in the form of submissions to ISI accredited journals. In the first paper, objectives are demonstrated with a vehicle detector incorporating a two stage GMM classifier, applied to a single feature space, namely Histogram of Oriented Gradients (HoG). While the second paper demonstrates objectives with a vehicle tracker using colour histograms (in RGB and HSV), with Gaussian Mixture Model (GMM) classifiers and a Kalman filter.
The proposed works are comparable to related works with testing performed on benchmark datasets. In the tracking domain for such platforms, tracking alone is insufficient. Adaptive detection and classification can assist in search space reduction, building of knowledge priors and improved target representations. Results show that the proposed approach improves performance and robustness. Findings also indicate potential further enhancements such as a multi-mode tracker with global and local tracking based on a combination of both papers
Object detection, recognition and re-identification in video footage
There has been a significant number of security concerns in recent times; as a result, security cameras have been installed to monitor activities and to prevent crimes in most public places. These analysis are done either through video analytic or forensic analysis operations on human observations. To this end, within the research context of this thesis, a proactive machine vision based military recognition system has been developed to help monitor activities in the military environment. The proposed object detection, recognition and re-identification systems have been presented in this thesis.
A novel technique for military personnel recognition is presented in this thesis. Initially the detected camouflaged personnel are segmented using a grabcut segmentation algorithm. Since in general a camouflaged personnel's uniform appears to be similar both at the top and the bottom of the body, an image patch is initially extracted from the segmented foreground image and used as the region of interest. Subsequently the colour and texture features are extracted from each patch and used for classification. A second approach for personnel recognition is proposed through the recognition of the badge on the cap of a military person. A feature matching metric based on the extracted Speed Up Robust Features (SURF) from the badge on a personnel's cap enabled the recognition of the personnel's arm of service.
A state-of-the-art technique for recognising vehicle types irrespective of their view angle is also presented in this thesis. Vehicles are initially detected and segmented using a Gaussian Mixture Model (GMM) based foreground/background segmentation algorithm. A Canny Edge Detection (CED) stage, followed by morphological operations are used as pre-processing stage to help enhance foreground vehicular object detection and segmentation. Subsequently, Region, Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) features are extracted from the refined foreground vehicle object and used as features for vehicle type recognition. Two different datasets with variant views of front/rear and angle are used and combined for testing the proposed technique.
For night-time video analytics and forensics, the thesis presents a novel approach to pedestrian detection and vehicle type recognition. A novel feature acquisition technique named, CENTROG, is proposed for pedestrian detection and vehicle type recognition in this thesis. Thermal images containing pedestrians and vehicular objects are used to analyse the performance of the proposed algorithms. The video is initially segmented using a GMM based foreground object segmentation algorithm. A CED based pre-processing step is used to enhance segmentation accuracy prior using Census Transforms for initial feature extraction. HOG features are then extracted from the Census transformed images and used for detection and recognition respectively of human and vehicular objects in thermal images.
Finally, a novel technique for people re-identification is proposed in this thesis based on using low-level colour features and mid-level attributes. The low-level colour histogram bin values were normalised to 0 and 1. A publicly available dataset (VIPeR) and a self constructed dataset have been used in the experiments conducted with 7 clothing attributes and low-level colour histogram features. These 7 attributes are detected using features extracted from 5 different regions of a detected human object using an SVM classifier. The low-level colour features were extracted from the regions of a detected human object. These 5 regions are obtained by human object segmentation and subsequent body part sub-division. People are re-identified by computing the Euclidean distance between a probe and the gallery image sets. The experiments conducted using SVM classifier and Euclidean distance has proven that the proposed techniques attained all of the aforementioned goals. The colour and texture features proposed for camouflage military personnel recognition surpasses the state-of-the-art methods. Similarly, experiments prove that combining features performed best when recognising vehicles in different views subsequent to initial training based on multi-views. In the same vein, the proposed CENTROG technique performed better than the state-of-the-art CENTRIST technique for both pedestrian detection and vehicle type recognition at night-time using thermal images. Finally, we show that the proposed 7 mid-level attributes and the low-level features results in improved performance accuracy for people re-identification
Recent Developments in Video Surveillance
With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued
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Automated Detection and Counting of Pedestrians on an Urban Roadside
This thesis implements an automated system that counts pedestrians with 85% accuracy. Two approaches have been considered and evaluated in terms of count accuracy, cost and ease of deployment. The first approach employs the Autoscope Solo Terra, a traffic camera which is widely used to monitor vehicular traffic. The Solo Terra supports an image processing-based detector that counts the number of objects crossing user-defined areas in the captured image. The count is updated based on the amount of movement across the selected regions. Therefore, a second approach has been considered that uses a histogram of oriented gradients (HoG), an advanced vision based algorithm proposed by Dalal et al. which distinguishes a pedestrian from a non-pedestrian based on an omega shape formed by the head and shoulders of a human being. The implemented detection software processes video frames that are streamed from a low-cost digital camera. The frames are divided into sub-regions which are scanned for an omega shape whenever movement is detected in those regions. It has been found that the HoG-based approach degrades in performance due to occlusion under dense pedestrian traffic conditions whereas the Solo Terra approach appears to be more robust. Undercounts and overcounts were encountered using the Solo Terra approach. To combat the disadvantages of both the approaches, they were integrated to form a single system where count is incremented predominantly using the Solo Terra. The HoG-based approach corrects the obtained count under certain conditions. A preliminary prototype of the integrated system has been verified
Automatic object classification for surveillance videos.
PhDThe recent popularity of surveillance video systems, specially located in urban
scenarios, demands the development of visual techniques for monitoring purposes.
A primary step towards intelligent surveillance video systems consists on automatic
object classification, which still remains an open research problem and the keystone
for the development of more specific applications.
Typically, object representation is based on the inherent visual features. However,
psychological studies have demonstrated that human beings can routinely categorise
objects according to their behaviour. The existing gap in the understanding
between the features automatically extracted by a computer, such as appearance-based
features, and the concepts unconsciously perceived by human beings but
unattainable for machines, or the behaviour features, is most commonly known
as semantic gap. Consequently, this thesis proposes to narrow the semantic gap
and bring together machine and human understanding towards object classification.
Thus, a Surveillance Media Management is proposed to automatically detect and
classify objects by analysing the physical properties inherent in their appearance
(machine understanding) and the behaviour patterns which require a higher level of
understanding (human understanding). Finally, a probabilistic multimodal fusion
algorithm bridges the gap performing an automatic classification considering both
machine and human understanding.
The performance of the proposed Surveillance Media Management framework
has been thoroughly evaluated on outdoor surveillance datasets. The experiments
conducted demonstrated that the combination of machine and human understanding
substantially enhanced the object classification performance. Finally, the inclusion
of human reasoning and understanding provides the essential information to bridge
the semantic gap towards smart surveillance video systems
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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
Unsupervised video indexing on audiovisual characterization of persons
Cette thèse consiste à proposer une méthode de caractérisation non-supervisée des intervenants dans les documents audiovisuels, en exploitant des données liées à leur apparence physique et à leur voix. De manière générale, les méthodes d'identification automatique, que ce soit en vidéo ou en audio, nécessitent une quantité importante de connaissances a priori sur le contenu. Dans ce travail, le but est d'étudier les deux modes de façon corrélée et d'exploiter leur propriété respective de manière collaborative et robuste, afin de produire un résultat fiable aussi indépendant que possible de toute connaissance a priori. Plus particulièrement, nous avons étudié les caractéristiques du flux audio et nous avons proposé plusieurs méthodes pour la segmentation et le regroupement en locuteurs que nous avons évaluées dans le cadre d'une campagne d'évaluation. Ensuite, nous avons mené une étude approfondie sur les descripteurs visuels (visage, costume) qui nous ont servis à proposer de nouvelles approches pour la détection, le suivi et le regroupement des personnes. Enfin, le travail s'est focalisé sur la fusion des données audio et vidéo en proposant une approche basée sur le calcul d'une matrice de cooccurrence qui nous a permis d'établir une association entre l'index audio et l'index vidéo et d'effectuer leur correction. Nous pouvons ainsi produire un modèle audiovisuel dynamique des intervenants.This thesis consists to propose a method for an unsupervised characterization of persons within audiovisual documents, by exploring the data related for their physical appearance and their voice. From a general manner, the automatic recognition methods, either in video or audio, need a huge amount of a priori knowledge about their content. In this work, the goal is to study the two modes in a correlated way and to explore their properties in a collaborative and robust way, in order to produce a reliable result as independent as possible from any a priori knowledge. More particularly, we have studied the characteristics of the audio stream and we have proposed many methods for speaker segmentation and clustering and that we have evaluated in a french competition. Then, we have carried a deep study on visual descriptors (face, clothing) that helped us to propose novel approches for detecting, tracking, and clustering of people within the document. Finally, the work was focused on the audiovisual fusion by proposing a method based on computing the cooccurrence matrix that allowed us to establish an association between audio and video indexes, and to correct them. That will enable us to produce a dynamic audiovisual model for each speaker
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