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

Segmentasi dan pengesanan objek bergerak dalam keadaan cuaca berjerebu dan berkabus

Segmentation and detection of moving object are very important in navigation applications to improve visibility of computer vision technology. The challenges to these issues are how these two issues address hazy and foggy weather. This situation affects technology and specifically the video data used to detect moving objects. This problem occurs due to the light that is scattered because of the fog and haze pixels which prevent light from penetrating resulting in over segmentation. Various methods have been used to improve accuracy and sensitivity in over segmentation but further enhancement is needed to improve the performance in the detection of moving objects. In this research, a new method is proposed to overcome over segmentation which is a combination between Gaussian Mixture Model and other filters based on their own specialities. The combined filters comprised Median Filter and Average Filter for over segmentation, Morphology Filter and Gaussian Filter to rebuild structure element of pixel object, and combination of Blob Analysis, Bounding Box and Kalman Filter to reduce False Positive detection. The combination of these filters is known as Object of Interest Movement (OIM). Qualitative and quantitative methods were used to make comparison with previous methods. Data comprised sources of haze recordings obtained from YouTube and open dataset from Karlsure. Comparative analysis of pictures and calculations of detection of objects were done. Result showed that the combined filters is capable of improving accuracy and sensitivity of the segmentation and detection which were 72.24% for foggy videos, and 76.73% in hazy weather. Based on the findings, the OIM method has proven its capability to improve the accuracy of segmentation and detection object without the need for enhancement to contrast an image

Adaptive Kernel Density Approximation and Its Applications to Real-Time Computer Vision

Density-based modeling of visual features is very common in computer vision research due to the uncertainty of observed data; so accurate and simple density representation is essential to improve the quality of overall systems. Even though various methods, either parametric or non-parametric, are proposed for density modeling, there is a significant trade-off between flexibility and computational complexity. Therefore, a new compact and flexible density representation is necessary, and the dissertation provides a solution to alleviate the problems as follows. First, we describe a compact and flexible representation of probability density functions using a mixture of Gaussians which is called Kernel Density Approximation (KDA). In this framework, the number of Gaussians components as well as the weight, mean, and covariance of each Gaussian component are determined automatically by mean-shift mode-finding procedure and curvature fitting. An original density function estimated by kernel density estimation is simplified into a compact mixture of Gaussians by the proposed method; memory requirements are dramatically reduced while incurring only a small amount of error. In order to adapt to variations of visual features, sequential kernel density approximation is proposed in which a sequential update of the density function is performed in linear time. Second, kernel density approximation is incorporated into a Bayesian filtering framework, and we design a Kernel-based Bayesian Filter (KBF). Particle filters have inherent limitations such as degeneracy or loss of diversity which are mainly caused by sampling from discrete proposal distribution. In kernel-based Bayesian filtering, every relevant probability density function is continuous and the posterior is simplified by kernel density approximation so as to propagate a compact form of the density function from step to step. Since the proposal distribution is continuous in this framework, the problems in conventional particle filters are alleviated. The sequential kernel density approximation technique is naturally applied to background modeling, and target appearance modeling for object tracking. Also, the kernel-based Bayesian filtering framework is applied to object tracking, which shows improved performance with a smaller number of samples. We demonstrate the performance of kernel density approximation and its application through various simulations and experiments with real videos

Soft computing and non-parametric techniques for effective video surveillance systems

Esta tesis propone varios objetivos interconectados para el diseño de un sistema de vídeovigilancia cuyo funcionamiento es pensado para un amplio rango de condiciones. Primeramente se propone una métrica de evaluación del detector y sistema de seguimiento basada en una mínima referencia. Dicha técnica es una respuesta a la demanda de ajuste de forma rápida y fácil del sistema adecuándose a distintos entornos. También se propone una técnica de optimización basada en Estrategias Evolutivas y la combinación de funciones de idoneidad en varios pasos. El objetivo es obtener los parámetros de ajuste del detector y el sistema de seguimiento adecuados para el mejor funcionamiento en una amplia gama de situaciones posibles Finalmente, se propone la construcción de un clasificador basado en técnicas no paramétricas que pudieran modelar la distribución de datos de entrada independientemente de la fuente de generación de dichos datos. Se escogen actividades detectables a corto plazo que siguen un patrón de tiempo que puede ser fácilmente modelado mediante HMMs. La propuesta consiste en una modificación del algoritmo de Baum-Welch con el fin de modelar las probabilidades de emisión del HMM mediante una técnica no paramétrica basada en estimación de densidad con kernels (KDE). _____________________________________This thesis proposes several interconnected objectives for the design of a video-monitoring system whose operation is thought for a wide rank of conditions. Firstly an evaluation technique of the detector and tracking system is proposed and it is based on a minimum reference or ground-truth. This technique is an answer to the demand of fast and easy adjustment of the system adapting itself to different contexts. Also, this thesis proposes a technique of optimization based on Evolutionary Strategies and the combination of fitness functions. The objective is to obtain the parameters of adjustment of the detector and tracking system for the best operation in an ample range of possible situations. Finally, it is proposed the generation of a classifier in which a non-parametric statistic technique models the distribution of data regardless the source generation of such data. Short term detectable activities are chosen that follow a time pattern that can easily be modeled by Hidden Markov Models (HMMs). The proposal consists in a modification of the Baum-Welch algorithm with the purpose of modeling the emission probabilities of the HMM by means of a nonparametric technique based on the density estimation with kernels (KDE)

Robust gesture recognition

It is a challenging problem to make a general hand gesture recognition system work in a practical operation environment. In this study, it is mainly focused on recognizing English letters and digits performed near the steering wheel of a car and captured by a video camera. Like most human computer interaction (HCI) scenarios, the in-car gesture recognition suffers from various robustness issues, including multiple human factors and highly varying lighting conditions. It therefore brings up quite a few research issues to be addressed. First, multiple gesturing alternatives may share the same meaning, which is not typical in most previous systems. Next, gestures may not be the same as expected because users cannot see what exactly has been written, which increases the gesture diversity significantly.In addition, varying illumination conditions will make hand detection trivial and thus result in noisy hand gestures. And most severely, users will tend to perform letters at a fast pace, which may result in lack of frames for well-describing gestures. Since users are allowed to perform gestures in free-style, multiple alternatives and variations should be considered while modeling gestures. The main contribution of this work is to analyze and address these challenging issues step-by-step such that eventually the robustness of the whole system can be effectively improved. By choosing color-space representation and performing the compensation techniques for varying recording conditions, the hand detection performance for multiple illumination conditions is first enhanced. Furthermore, the issues of low frame rate and different gesturing tempo will be separately resolved via the cubic B-spline interpolation and i-vector method for feature extraction. Finally, remaining issues will be handled by other modeling techniques such as sub-letter stroke modeling. According to experimental results based on the above strategies, the proposed framework clearly improved the system robustness and thus encouraged the future research direction on exploring more discriminative features and modeling techniques.Ph.D

Quality-Driven video analysis for the improvement of foreground segmentation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones.Fecha de lectura: 15-06-2018It was partially supported by the Spanish Government (TEC2014-53176-R, HAVideo

Learning object behaviour models

The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their gait, interpret a facial expression as that of surprise, or identify suspicious behaviour in the movements of an individual within a car-park. Machine vision systems wishing to exploit such behavioural knowledge have been limited by the inaccuracies inherent in hand-crafted models and the absence of a unified framework for the perception of powerful behaviour models. The research described in this thesis attempts to address these limitations, using a statistical modelling approach to provide a framework in which detailed behavioural knowledge is acquired from the observation of long image sequences. The core of the behaviour modelling framework is an optimised sample-set representation of the probability density in a behaviour space defined by a novel temporal pattern formation strategy. This representation of behaviour is both concise and accurate and facilitates the recognition of actions or events and the assessment of behaviour typicality. The inclusion of generative capabilities is achieved via the addition of a learnt stochastic process model, thus facilitating the generation of predictions and realistic sample behaviours. Experimental results demonstrate the acquisition of behaviour models and suggest a variety of possible applications, including automated visual surveillance, object tracking, gesture recognition, and the generation of realistic object behaviours within animations, virtual worlds, and computer generated film sequences. The utility of the behaviour modelling framework is further extended through the modelling of object interaction. Two separate approaches are presented, and a technique is developed which, using learnt models of joint behaviour together with a stochastic tracking algorithm, can be used to equip a virtual object with the ability to interact in a natural way. Experimental results demonstrate the simulation of a plausible virtual partner during interaction between a user and the machine