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

Video anomaly detection and localization by local motion based joint video representation and OCELM

Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions’ motion in the spatio-temporal cuboid extracted from video, which can implicitly reflect the structural information of foreground and depict foreground motion more precisely than the normal HOF descriptor. To locate the video foreground more accurately, we propose a new Robust PCA based foreground localization scheme. ULGP-OF descriptor, which seamlessly combines the classic 2D texture descriptor LGP and optical flow, is proposed to describe the motion statistics of local region texture in the areas located by the foreground localization scheme. Both SL-HOF and ULGP-OF are shown to be more discriminative than existing video descriptors in anomaly detection. To model features of normal video events, we introduce the newly-emergent one-class Extreme Learning Machine (OCELM) as the data description algorithm. With a tremendous reduction in training time, OCELM can yield comparable or better performance than existing algorithms like the classic OCSVM, which makes our approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data. The proposed approach is tested on UCSD ped1, ped2 and UMN datasets, and experimental results show that our approach can achieve state-of-the-art results in both video anomaly detection and localization task.This work was supported by the National Natural Science Foundation of China (Project nos. 60970034, 61170287, 61232016)

A Survey on Unusual Event Detection in Videos

As the usage of CCTV cameras in outdoor and indoor locations has increased significantly, one needs to design a system to detect the unusual events, at the time of its occurrence. Computer vision is used for Human Action recognition, which has been widely implemented in the systems, but unusual event detection is lately entering into the limelight. In order to detect the unusual events, supervised techniques, semi-supervised techniques and unsupervised techniques have been adopted. Social force model (SFM) and Force field are used to model the interaction among crowds. Only normal events training samples is not sufficient for detection of unusual events. Double sparse representation has been used as a solution to this, which includes normal and abnormal training data. To develop an intelligent video surveillance system, behavioural representation and behavioural modelling techniques are used. Various machine learning techniques to identify unusual events include: Graph modelling and matching, object trajectory based, object silhouettes based and pixel based approaches. Kullback–Leibler (KL) divergence, Quaternion Discrete Cosine Transformation (QDCT) analysis, hidden Markov model (HMM) and histogram of oriented contextual gradient (HOCG) descriptor are some of the models used are used for detecting unusual events. This paper briefly discusses the above mentioned strategies and pay attention on their pros and cons

Detecção de eventos complexos em vídeos baseada em ritmos visuais

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O reconhecimento de eventos complexos em vídeos possui várias aplicações práticas relevantes, alavancadas pela grande disponibilidade de câmeras digitais instaladas em aeroportos, estações de ônibus e trens, centros de compras, estádios, hospitais, escolas, prédios, estradas, entre vários outros locais. Avanços na tecnologia digital têm aumentado as capacidades dos sistemas em reconhecer eventos em vídeos por meio do desenvolvimento de dispositivos com alta resolução, dimensões físicas pequenas e altas taxas de amostragem. Muitos trabalhos disponíveis na literatura têm explorado o tema a partir de diferentes pontos de vista. Este trabalho apresenta e avalia uma metodologia para extrair características dos ritmos visuais no contexto de detecção de eventos em vídeos. Um ritmo visual pode ser visto com a projeção de um vídeo em uma imagem, tal que a tarefa de análise de vídeos é reduzida a um problema de análise de imagens, beneficiando-se de seu baixo custo de processamento em termos de tempo e complexidade. Para demonstrar o potencial do ritmo visual na análise de vídeos complexos, três problemas da área de visão computacional são selecionados: detecção de eventos anômalos, classificação de ações humanas e reconhecimento de gestos. No primeiro problema, um modelo e? aprendido com situações de normalidade a partir dos rastros deixados pelas pessoas ao andar, enquanto padro?es representativos das ações são extraídos nos outros dois problemas. Nossa hipo?tese e? de que vídeos similares produzem padro?es semelhantes, tal que o problema de classificação de ações pode ser reduzido a uma tarefa de classificação de imagens. Experimentos realizados em bases públicas de dados demonstram que o método proposto produz resultados promissores com baixo custo de processamento, tornando-o possível aplicar em tempo real. Embora os padro?es dos ritmos visuais sejam extrai?dos como histograma de gradientes, algumas tentativas para adicionar características do fluxo o?tico são discutidas, além de estratégias para obter ritmos visuais alternativosAbstract: The recognition of complex events in videos has currently several important applications, particularly due to the wide availability of digital cameras in environments such as airports, train and bus stations, shopping centers, stadiums, hospitals, schools, buildings, roads, among others. Moreover, advances in digital technology have enhanced the capabilities for detection of video events through the development of devices with high resolution, small physical size, and high sampling rates. Many works available in the literature have explored the subject from different perspectives. This work presents and evaluates a methodology for extracting a feature descriptor from visual rhythms of video sequences in order to address the video event detection problem. A visual rhythm can be seen as the projection of a video onto an image, such that the video analysis task can be reduced into an image analysis problem, benefiting from its low processing cost in terms of time and complexity. To demonstrate the potential of the visual rhythm in the analysis of complex videos, three computer vision problems are selected in this work: abnormal event detection, human action classification, and gesture recognition. The former problem learns a normalcy model from the traces that people leave when they walk, whereas the other two problems extract representative patterns from actions. Our hypothesis is that similar videos produce similar patterns, therefore, the action classification problem is reduced into an image classification task. Experiments conducted on well-known public datasets demonstrate that the method produces promising results at high processing rates, making it possible to work in real time. Even though the visual rhythm features are mainly extracted as histogram of gradients, some attempts for adding optical flow features are discussed, as well as strategies for obtaining alternative visual rhythmsMestradoCiência da ComputaçãoMestre em Ciência da Computação1570507, 1406910, 1374943CAPE