Crowd Behavior Understanding through SIOF Feature Analysis

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over time. A non-linear SVM has then been adopted to classify dominant or large-scale crow d abnormal behaviors. The work reported has focused on: 1) online (or near real-time) detection of moving objects through a background subtraction model, namely ViBe; and to identify the saliency information as a spatial feature in addition to the optical flow of the motion foreground as the temporal feature; 2) to combine the extracted spatial and temporal features into a novel SIOF descriptor that encapsulates the global movement characteristic of a crowd; 3) the optimization of a nonlinear support vector machine (SVM) as classifier to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the BEHAVE database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements in terms of the accuracy and efficiency for detecting crowd anomalies

Crowd Saliency Detection via Global Similarity Structure

It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveillance. This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure. The global similarity structure representation allows the discovery of the intrinsic manifold of the motion dynamics, which could not be captured by the low-level representation. Ranking is then performed on the global similarity structure to identify a set of extrema. The proposed approach is unsupervised so learning stage is eliminated. Experimental results on public datasets demonstrates the effectiveness of exploiting such extrema in identifying salient regions in various crowd scenarios that exhibit crowding, local irregular motion, and unique motion areas such as sources and sinks.Comment: Accepted in ICPR 2014 (Oral). Mei Kuan Lim and Ven Jyn Kok share equal contribution

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.Comment: Oral paper in BMVC 201

Rare Events Detection and Localization In Crowded Scenes Based On Flow Signature

We introduce in this paper a novel method for rare events detection based on the optical flow signature. It aims to automatically highlight regions in videos where rare events are occurring. This kind of method can be used as an important step for many applications such as Closed-Circuit Television (CCTV) monitoring systems in order to reduce the cognitive effort of the operators by focusing their attention on the interesting regions. The proposed method exploits the properties of the Discrete Cosine Transform (DCT) applied to the magnitude and orientation maps of the optical flow. The output of the algorithm is a map where each pixel has a saliency score that indicates the presence of irregular motion regard to the scene. Based on the one class Support Vectors Machine (SVM) algorithm, a model of the frequent events is created and the rare events detection can be performed by using this model. The DCT is faster, easy to compute and gives interesting information to detect spatial irregular patterns in images [1]. Our method does not rely on any prior information of the scene and uses the saliency score as a feature descriptor. We demonstrate the potential of the proposed method on the publicly available videos dataset UCSD and show that it is competitive and outperforms some the state-of-the-art methods

Análise de multidões usando coerência de vizinhança local

Large numbers of crowd analysis methods using computer vision have been developed in the past years. This dissertation presents an approach to explore characteristics inherent to human crowds – proxemics, and neighborhood relationship – with the purpose of extracting crowd features and using them for crowd flow estimation and anomaly detection and localization. Given the optical flow produced by any method, the proposed approach compares the similarity of each flow vector and its neighborhood using the Mahalanobis distance, which can be obtained in an efficient manner using integral images. This similarity value is then used either to filter the original optical flow or to extract features that describe the crowd behavior in different resolutions, depending on the radius of the personal space selected in the analysis. To show that the extracted features are indeed relevant, we tested several classifiers in the context of abnormality detection. More precisely, we used Recurrent Neural Networks, Dense Neural Networks, Support Vector Machines, Random Forest and Extremely Random Trees. The two developed approaches (crowd flow estimation and abnormality detection) were tested on publicly available datasets involving human crowded scenarios and compared with state-of-the-art methods.Métodos para análise de ambientes de multidões são amplamente desenvolvidos na área de visão computacional. Esta tese apresenta uma abordagem para explorar características inerentes às multidões humanas - comunicação proxêmica e relações de vizinhança - para extrair características de multidões e usá-las para estimativa de fluxo de multidões e detecção e localização de anomalias. Dado o fluxo óptico produzido por qualquer método, a abordagem proposta compara a similaridade de cada vetor de fluxo e sua vizinhança usando a distância de Mahalanobis, que pode ser obtida de maneira eficiente usando imagens integrais. Esse valor de similaridade é então utilizado para filtrar o fluxo óptico original ou para extrair informações que descrevem o comportamento da multidão em diferentes resoluções, dependendo do raio do espaço pessoal selecionado na análise. Para mostrar que as características são realmente relevantes, testamos vários classificadores no contexto da detecção de anormalidades. Mais precisamente, usamos redes neurais recorrentes, redes neurais densas, máquinas de vetores de suporte, floresta aleatória e árvores extremamente aleatórias. As duas abordagens desenvolvidas (estimativa do fluxo de multidões e detecção de anormalidades) foram testadas em conjuntos de dados públicos, envolvendo cenários de multidões humanas e comparados com métodos estado-da-arte