Discriminative Dictionary Learning with Motion Weber Local Descriptor for Violence Detection

© 1991-2012 IEEE. Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in developing an algorithm that can detect violence in surveillance videos with high performance. In this paper, following our recently proposed idea of motion Weber local descriptor (WLD), we make two major improvements and propose a more effective and efficient algorithm for detecting violence from motion images. First, we propose an improved WLD (IWLD) to better depict low-level image appearance information, and then extend the spatial descriptor IWLD by adding a temporal component to capture local motion information and hence form the motion IWLD. Second, we propose a modified sparse-representation-based classification model to both control the reconstruction error of coding coefficients and minimize the classification error. Based on the proposed sparse model, a class-specific dictionary containing dictionary atoms corresponding to the class labels is learned using class labels of training samples. With this learned dictionary, not only the representation residual but also the representation coefficients become discriminative. A classification scheme integrating the modified sparse model is developed to exploit such discriminative information. The experimental results on three benchmark data sets have demonstrated the superior performance of the proposed approach over the state of the arts

MoWLD: a robust motion image descriptor for violence detection

© 2015, Springer Science+Business Media New York. Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bag-of-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the non-parametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts

Violence detection based on spatio-temporal feature and fisher vector

© Springer Nature Switzerland AG 2018. A novel framework based on local spatio-temporal features and a Bag-of-Words (BoW) model is proposed for violence detection. The framework utilizes Dense Trajectories (DT) and MPEG flow video descriptor (MF) as feature descriptors and employs Fisher Vector (FV) in feature coding. DT and MF algorithms are more descriptive and robust, because they are combinations of various feature descriptors, which describe trajectory shape, appearance, motion and motion boundary, respectively. FV is applied to transform low level features to high level features. FV method preserves much information, because not only the affiliations of descriptors are found in the codebook, but also the first and second order statistics are used to represent videos. Some tricks, that PCA, K-means++ and codebook size, are used to improve the final performance of video classification. In comprehensive consideration of accuracy, speed and application scenarios, the proposed method for violence detection is analysed. Experimental results show that the proposed approach outperforms the state-of-the-art approaches for violence detection in both crowd scenes and non-crowd scenes

MoWLD: A Robust Motion Image Descriptor for Violence Detection

Abstract Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bagof-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the nonparametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts

Detecção de eventos violentos em sequências de vídeos baseada no operador histograma da transformada census

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Sistemas de vigilância em sequências de vídeo têm sido amplamente utilizados para o monitoramento de cenas em diversos ambientes, tais como aeroportos, bancos, escolas, indústrias, estações de ônibus e trens, rodovias e lojas. Devido à grande quantidade de informação obtida pelas câmeras de vigilância, o uso de inspeção visual por operadores de câmera se torna uma tarefa cansativa e sujeita a falhas, além de consumir muito tempo. Um desafio é o desenvolvimento de sistemas inteligentes de vigilância capazes de analisar longas sequências de vídeos capturadas por uma rede de câmeras de modo a identificar um determinado comportamento. Neste trabalho, foram propostas e avaliadas diversas técnicas de classificação, tendo como base o operador CENTRIST (Histograma da Transformada Census), no contexto de identificação de eventos violentos em cenas de vídeo. Adicionalmente, foram avaliados outros descritores tradicionais, como HoG (Histograma de Gradientes Orientados), HOF (Histograma do Fluxo Óptico) e descritores extraídos a partir de modelos de aprendizado de máquina profundo pré-treinados. De modo a permitir a avaliação apenas em regiões de interesse presentes nos quadros dos vídeos, técnicas para remoção do fundo da cena. Uma abordagem baseada em janela deslizante foi utilizada para avaliar regiões menores da cena em combinação com um critério de votação. A janela deslizante é então aplicada juntamente com uma filtragem de blocos utilizando fluxo óptico da cena. Para demonstrar a efetividade de nosso método para discriminar violência em cenas de multidões, os resultados obtidos foram comparados com outras abordagens disponíveis na literatura em duas bases de dados públicas (Violence in Crowds e Hockey Fights). A eficácia da combinação entre CENTRIST e HoG foi demonstrada em comparação com a utilização desses operadores individualmente. A combinação desses operadores obteve aproximadamente 88% contra 81% utilizando apenas HoG e 86% utilizando CENTRIST. A partir do refinamento do método proposto, foi identificado que avaliar blocos do quadro com a abordagem de janela deslizante tornou o método mais eficaz. Técnicas para geração de palavras visuais com codificação esparsa, medida de distância com um modelo de misturas Gaussianas e medida de distância entre agrupamentos também foram avaliadas e discutidas. Além disso, também foi avaliado calcular dinamicamente o limiar de votação, o que trouxe resultados melhores em alguns casos. Finalmente, formas de restringir os atores presentes nas cenas utilizando fluxo óptico foram analisadas. Utilizando o método de Otsu para calcular o limiar do fluxo óptico da cena a eficiência supera nossos resultados mais competitivos: 91,46% de acurácia para a base Violence in Crowds e 92,79% para a base Hockey FightsAbstract: Surveillance systems in video sequences have been widely used to monitor scenes in various environments, such as airports, banks, schools, industries, bus and train stations, highways and stores. Due to the large amount of information obtained via surveillance cameras, the use of visual inspection by camera operators becomes a task subject to fatigue and failure, in addition to consuming a lot of time. One challenge is the development of intelligent surveillance systems capable of analyzing long video sequences captured by a network of cameras in order to identify a certain behavior. In this work, we propose and analyze the use of several classification techniques, based on the CENTRIST (Transformation Census Histogram) operator, in the context of identifying violent events in video scenes. Additionally, we evaluated other traditional descriptors, such as HoG (Oriented Gradient Histogram), HOF (Optical Flow Histogram) and descriptors extracted from pre-trained deep machine learning models. In order to allow the evaluation only in regions of interest present in the video frames, we investigated techniques for removing the background from the scene. A sliding window-based approach was used to assess smaller regions of the scene in combination with a voting criterion. The sliding window is then applied along with block filtering using the optical flow of the scene. To demonstrate the effectiveness of our method for discriminating violence in crowd scenes, we compared the results to other approaches available in the literature in two public databases (Violence in Crowds and Hockey Fights). The combination of CENTRIST and HoG was demonstrated in comparison to the use of these operators individually. The combination of both operators obtained approximately 88% against 81% using only HoG and 86% using CENTRIST. From the refinement of the proposed method, we identified that evaluating blocks of the frame with the sliding window-based approach made the method more effective. Techniques for generating a codebook with sparse coding, distance measurement with a Gaussian mixture model and distance measurement between clusters were evaluated and discussed. Also we dynamically calculate the threshold for class voting, which obtained superior results in some cases. Finally, strategies for restricting the actors present in the scenes using optical flow were analyzed. By using the Otsu¿s method to calculate the threshold from the optical flow at the scene, the effectiveness surpasses our most competitive results: 91.46% accuracy for the Violence in Crowds dataset and 92.79% for the Hockey Fights datasetMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Visual Concept Detection in Images and Videos

The rapidly increasing proliferation of digital images and videos leads to a situation where content-based search in multimedia databases becomes more and more important. A prerequisite for effective image and video search is to analyze and index media content automatically. Current approaches in the field of image and video retrieval focus on semantic concepts serving as an intermediate description to bridge the “semantic gap” between the data representation and the human interpretation. Due to the large complexity and variability in the appearance of visual concepts, the detection of arbitrary concepts represents a very challenging task. In this thesis, the following aspects of visual concept detection systems are addressed: First, enhanced local descriptors for mid-level feature coding are presented. Based on the observation that scale-invariant feature transform (SIFT) descriptors with different spatial extents yield large performance differences, a novel concept detection system is proposed that combines feature representations for different spatial extents using multiple kernel learning (MKL). A multi-modal video concept detection system is presented that relies on Bag-of-Words representations for visual and in particular for audio features. Furthermore, a method for the SIFT-based integration of color information, called color moment SIFT, is introduced. Comparative experimental results demonstrate the superior performance of the proposed systems on the Mediamill and on the VOC Challenge. Second, an approach is presented that systematically utilizes results of object detectors. Novel object-based features are generated based on object detection results using different pooling strategies. For videos, detection results are assembled to object sequences and a shot-based confidence score as well as further features, such as position, frame coverage or movement, are computed for each object class. These features are used as additional input for the support vector machine (SVM)-based concept classifiers. Thus, other related concepts can also profit from object-based features. Extensive experiments on the Mediamill, VOC and TRECVid Challenge show significant improvements in terms of retrieval performance not only for the object classes, but also in particular for a large number of indirectly related concepts. Moreover, it has been demonstrated that a few object-based features are beneficial for a large number of concept classes. On the VOC Challenge, the additional use of object-based features led to a superior performance for the image classification task of 63.8% mean average precision (AP). Furthermore, the generalization capabilities of concept models are investigated. It is shown that different source and target domains lead to a severe loss in concept detection performance. In these cross-domain settings, object-based features achieve a significant performance improvement. Since it is inefficient to run a large number of single-class object detectors, it is additionally demonstrated how a concurrent multi-class object detection system can be constructed to speed up the detection of many object classes in images. Third, a novel, purely web-supervised learning approach for modeling heterogeneous concept classes in images is proposed. Tags and annotations of multimedia data in the WWW are rich sources of information that can be employed for learning visual concepts. The presented approach is aimed at continuous long-term learning of appearance models and improving these models periodically. For this purpose, several components have been developed: a crawling component, a multi-modal clustering component for spam detection and subclass identification, a novel learning component, called “random savanna”, a validation component, an updating component, and a scalability manager. Only a single word describing the visual concept is required to initiate the learning process. Experimental results demonstrate the capabilities of the individual components. Finally, a generic concept detection system is applied to support interdisciplinary research efforts in the field of psychology and media science. The psychological research question addressed in the field of behavioral sciences is, whether and how playing violent content in computer games may induce aggression. Therefore, novel semantic concepts most notably “violence” are detected in computer game videos to gain insights into the interrelationship of violent game events and the brain activity of a player. Experimental results demonstrate the excellent performance of the proposed automatic concept detection approach for such interdisciplinary research

Modeling Semi-Bounded Support Data using Non-Gaussian Hidden Markov Models with Applications

With the exponential growth of data in all formats, and data categorization rapidly becoming one of the most essential components of data analysis, it is crucial to research and identify hidden patterns in order to extract valuable information that promotes accurate and solid decision making. Because data modeling is the first stage in accomplishing any of these tasks, its accuracy and consistency are critical for later development of a complete data processing framework. Furthermore, an appropriate distribution selection that corresponds to the nature of the data is a particularly interesting subject of research. Hidden Markov Models (HMMs) are some of the most impressively powerful probabilistic models, which have recently made a big resurgence in the machine learning industry, despite having been recognized for decades. Their ever-increasing application in a variety of critical practical settings to model varied and heterogeneous data (image, video, audio, time series, etc.) is the subject of countless extensions. Equally prevalent, finite mixture models are a potent tool for modeling heterogeneous data of various natures. The over-use of Gaussian mixture models for data modeling in the literature is one of the main driving forces for this thesis. This work focuses on modeling positive vectors, which naturally occur in a variety of real-life applications, by proposing novel HMMs extensions using the Inverted Dirichlet, the Generalized Inverted Dirichlet and the BetaLiouville mixture models as emission probabilities. These extensions are motivated by the proven capacity of these mixtures to deal with positive vectors and overcome mixture models’ impotence to account for any ordering or temporal limitations relative to the information. We utilize the aforementioned distributions to derive several theoretical approaches for learning and deploying Hidden Markov Modelsinreal-world settings. Further, we study online learning of parameters and explore the integration of a feature selection methodology. Extensive experimentation on highly challenging applications ranging from image categorization, video categorization, indoor occupancy estimation and Natural Language Processing, reveals scenarios in which such models are appropriate to apply, and proves their effectiveness compared to the extensively used Gaussian-based models

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications