Feature extraction techniques for abandoned object classification in video surveillance

We address the problem of abandoned object classification in video surveillance. Our aim is to determine (i) which feature extraction technique proves more useful for accurate object classification in a video surveillance context (scale invariant image transform (SIFT) keypoints vs. geometric primitive features), and (ii) how the resulting features affect classification accuracy and false positive rates for different classification schemes used. Objects are classified into four different categories: bag (s), person (s), trolley (s), and group (s) of people. Our experimental results show that the highest recognition accuracy and the lowest false alarm rate are achieved by building a classifier based on our proposed set of statistics of geometric primitives' features. Moreover, classification performance based on this set of features proves to be more invariant across different learning algorithms. © 2008 IEEE

Mixtures of Gaussian distributions under linear dimensionality reduction

High dimensional spaces pose a serious challenge to the learning process. It is a combination of limited number of samples and high dimensions that positions many problems under the "curse of dimensionality", which restricts severely the practical application of density estimation. Many techniques have been proposed in the past to discover embedded, locally-linear manifolds of lower dimensionality, including the mixture of Principal Component Analyzers, the mixture of Probabilistic Principal Component Analyzers and the mixture of Factor Analyzers. In this paper, we present a mixture model for reducing dimensionality based on a linear transformation which is not restricted to be orthogonal. Two methods are proposed for the learning of all the transformations and mixture parameters: the first method is based on an iterative maximum-likelihood approach and the second is based on random transformations and fixed (non iterative) probability functions. For experimental validation, we have used the proposed model for maximum-likelihood classification of five "hard" data sets including data sets from the UCI repository and the authors' own. Moreover, we compared the classification performance of the proposed method with that of other popular classifiers including the mixture of Probabilistic Principal Component Analyzers and the Gaussian mixture model. In all cases but one, the accuracy achieved by the proposed method proved the highest, with increases with respect to the runner-up ranging from 0.2% to 5.2%

MLiT: Mixtures of Gaussians under linear transformations

The curse of dimensionality hinders the effectiveness of density estimation in high dimensional spaces. Many techniques have been proposed in the past to discover embedded, locally linear manifolds of lower dimensionality, including the mixture of principal component analyzers, the mixture of probabilistic principal component analyzers and the mixture of factor analyzers. In this paper, we propose a novel mixture model for reducing dimensionality based on a linear transformation which is not restricted to be orthogonal nor aligned along the principal directions. For experimental validation, we have used the proposed model for classification of five "hard" data sets and compared its accuracy with that of other popular classifiers. The performance of the proposed method has outperformed that of the mixture of probabilistic principal component analyzers on four out of the five compared data sets with improvements ranging from 0. 5 to 3.2%. Moreover, on all data sets, the accuracy achieved by the proposed method outperformed that of the Gaussian mixture model with improvements ranging from 0.2 to 3.4%. © 2011 Springer-Verlag London Limited

Effective feature sets and dimensionality reduction for object classification

University of Technology, Sydney. Faculty of Engineering and Information Technology.NO FULL TEXT AVAILABLE. Access is restricted indefinitely. The hardcopy may be available for consultation at the UTS Library.NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Recognition of visual objects into classes of interest is a long-explored area of computer vision and pattern recognition. The development of robust object recognition systems is important for a wide range of applications such as video surveillance, medical image analysis, face recognition, and many others. However, it is an extremely challenging task for computers to recognize objects as they can occur under different viewpoints, scale, illumination, occlusions, and background. The high variations among objects within the same category add to these challenges. For effective classification, it is necessary to have an effective feature set, for representing the objects, and a strong learning method, for classification. In this work, we first target the problem of recognition of abandoned objects in video surveillance systems. It is a difficult task where the challenges above become more evident and thus further research is needed to solve it. To this aim, we propose a novel feature set based on statistics of various features such as line segments, circles, corners, and global shape descriptors such as fitted ellipses and bounding boxes. We show the invariance of the proposed feature set to different data set types and learning algorithms. Moreover, to further prove the robustness of the proposed feature set, we compare it with other feature sets that are based on local regions (Scale Invariant Feature Transform (SIFT) keypoints). The classification results based on the proposed feature set achieved a 82.8% detection rate and a 5.7% false positives rate in classifying images of the four objects of interest: trolley, bag, person, and group of people. Moreover, classification based on the proposed feature set outperformed that based on SIFT keypoints by providing an average 23.8% higher detection rate and 7.9% lower false alarm rate. These results are promising considering the various challenges in a surveillance environment. Given the high dimensionality of the feature set used to represent the objects (44 dimensions) and because of the different complexity aspects associated with such a high dimensional space, in the second part of this thesis we propose a novel mixture model for reducing dimensionality: MLiT: Mixture of Gaussians under Linear Transformations. Each component in the mixture consists of a linear transformation (which is not restricted to be orthogonal) projecting the original data onto a subspace and a Gaussian distribution fitted on the projected data. Two methods are proposed for optimizing the model: the first method is based on a maximum-likelihood approach and the second is based on random projections. To validate the proposed model, we used it for maximum-likelihood classification of five “hard” data sets, including our video surveillance data set and four data sets from the UCI repository. We also compared the accuracy results of the proposed model with that of other popular classifiers. The accuracy achieved by the proposed method has outperformed that of other classifiers based on a similar classification approach (generative classifiers based on mixture models), in all cases but one, with improvements ranging from 0.2% to 5.2%. In order to further improve the classification performance of MLiT, we also propose BoostMLiT: Boosting Mixture of Gaussians under Linear Transformations. It integrates MLiT within the framework of AdaBoost, which is a widely applied method for boosting. In the cases where boosting has been feasible (i.e., the cases with low training error), the proposed method has proved effective in enhancing the performance of MLiT with improvements of up to 12.8%. In this work, we have contributed to resolving outstanding issues towards more accurate classification of objects in a video surveillance environment. Moreover, the developed mixture model for dimensionality reduction has proved successful for solving a number of challenging classification tasks. Further to the applications considered in this thesis, the proposed mixture model can be used for modelling densities in high dimensional spaces in a variety of other applications, including weighted maximum likelihood, hidden Markov models, and discrete state-space models in general

Comparative performance analysis of feature sets for abandoned object classification

Accurate classification of abandoned objects is crucial in video surveillance systems. In this paper, we experiment with different validation techniques (hold-out and 10-fold cross validation), with the aim of determining which feature set proves more useful for accurate object classification in a video surveillance context (scale invariant image transform (SIFT) keypoints vs. geometric primitive features). Moreover, we show how the resulting features affect classification performance across different classifiers. We also further analyze the best performing classifier in order to have better understanding of its classification results. Objects are classified into four different categories: bag (s), person (s), trolley (s), and group (s) of people. Our experimental results show that the highest recognition accuracy and the lowest false alarm rate are achieved by building a classifier based on our proposed set of statistics of geometric primitives' features. This set of features maximizes inter-class separation and simplifies the classification process. Classification based on this set of features thus outperforms the second best approach based on SIFT keypoint histograms by providing on average 22% higher recognition accuracy and 7% lower false alarm rate. © 2008 IEEE

Boosting mixtures of gaussians under normalized linear transformations for image classification

We address the problem of image classification. Our aim is to improve the performance of MLiT: mixture of Gaussians under Linear transformations, a feature-based classifier proposed in [1] aiming to reduce dimensionality based on a linear transformation which is not restricted to be orthogonal. Boosting might offer an interesting solution by improving the performance of a given base classification algorithm. In this paper, we propose to integrate MLiT within the framework of AdaBoost, which is a widely applied method for boosting. For experimental validation, we have evaluated the proposed method on the four UCI data sets (Vehicle, OpticDigit, WDBC, WPBC) [2] and the author's own. Boosting has proved capable of enhancing the performance of the base classifier on two data sets with improvements of up to 12.8%

Towards Automatic Abandoned Object Classification in Visual Surveillance Systems

One of the core components of any visual surveillance system is object classification, where detected objects are classified into different categories of interest. Although in airports or train stations, abandoned objects are mainly luggage or trolleys, none of the existing works in the literature have attempted to classify or recognize trolleys. In this paper, we analyze and classify images of trolleys, bags, persons, and groups of people by using various shape features. We conducted a set of experiments with a number of uncluttered (images collected from the Internet with clear background) and cluttered images (images segmented out from the background in real videos) using various criteria. Our experimental results show that the features extracted enable 100% recognition accuracy for the trolley category. For our four-class object recognition problem, we achieved an overall recognition accuracy of 83.3% and an average false positive rate of 6%

Maximum-likelihood dimensionality reduction in Gaussian mixture models with an application to object classification

Accurate classification of objects of interest for video surveillance is difficult due to occlusions, deformations and variable views/illumination. The adopted feature sets tend to overcome these issues by including many and complementary features; however, their large dimensionality poses an intrinsic challenge to the classification task. In this paper, we present a novel technique providing maximum-likelihood dimensionality reduction in Gaussian mixture models for classification. The technique, called hereafter mixture of maximum-likelihood normalized projections (mixture of ML-NP), was used in this work to classify a 44-dimensional data set into 4 classes (bag, trolley, single person, group of people). The accuracy achieved on an independent test set is 98% vs. 80% of the runner-up (MultiBoost/AdaBoost). © 2008 IEEE

Mixtures of Normalized Linear Projections

High dimensional spaces pose a challenge to any classification task. In fact, these spaces contain much redundancy and it becomes crucial to reduce the dimensionality of the data to improve analysis, density modeling, and classification. In this paper, we present a method for dimensionality reduction in mixture models and its use in classification. For each component of the mixture, the data are projected by a linear transformation onto a lower-dimensional space. Subsequently, the projection matrices and the densities in such compressed spaces are learned by means of an Expectation Maximization (EM) algorithm. However, two main issues arise as a result of implementing this approach, namely: 1) the scale of the densities can be different across the mixture components and 2) a singularity problem may occur. We suggest solutions to these problems and validate the proposed method on three image data sets from the UCI Machine Learning Repository. The classification performance is compared with that of a mixture of probabilistic principal component analysers (MPPCA). Across the three data sets, our accuracy always compares favourably, with improvements ranging from 2.5% to 35.4%. © 2009 Springer Berlin Heidelberg