Online real-time crowd behavior detection in video sequences

Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach

Wavelet-based Texture Model for Crowd Dynamic Analysis

Crowd event detection techniques aim at solving real-world surveillance problems, such as detecting crowd anomaly and tracking specific person in a highly dynamic crowd scene. In this paper, we proposed an innovate texture-based analysis method to model crowd dynamics and us it to distinguish the crowd behaviours. To describe complicated crowd scenes, homogeneous random features have been deployed in the research for behavioural template matching. Experiment results have shown that the anomaly appearing in crowd scenes can be effectively and efficiently identified by using the devised methods

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

Contour Based Tracking for Driveway Entrance Counting System

Managing vehicle in free-flow entrance is tiring to do manually by a guard control especially due to the increase in transportation demand. Providing an accurate vehicle counting approach is vital for traffic management and it will surely be an essential part in tomorrow's smart cities. Therefore, the main objective of this paper is to propose a more accurate vehicle counter by using the tracking and heuristic rules approaches. EzCam v1.0 is a vehicle surveillance system for a free-flow entrance where a module of vehicle counting based on proposed idea has been applied. The proposed method does not require high computational resources more than any relatively affordable non task specific hardware. It employs single threshold, contour extraction and sequential frame analysis and finally, vehicle counting process subsequently. The tracking-based method employs foreground object detection method and a mechanism for object filtering approach as compared to Chris Dahms approach which does not consider any object rejection and accept all contour information as relevant to be counted as vehicles. As a result, EzCam v1.0 which utilizes the exploited contour-based approach is able to achieve up to 94 percent of accuracy rate and outperforms the classic Chris Dahms method which obtained an accuracy of 88 percent. Therefore, the Exploited Contour based tracking method helps vehicle counting system to perform better accuracy in comparison to Chris Dahms approach

Subspace discovery for video anomaly detection

PhDIn automated video surveillance anomaly detection is a challenging task. We address this task as a novelty detection problem where pattern description is limited and labelling information is available only for a small sample of normal instances. Classification under these conditions is prone to over-fitting. The contribution of this work is to propose a novel video abnormality detection method that does not need object detection and tracking. The method is based on subspace learning to discover a subspace where abnormality detection is easier to perform, without the need of detailed annotation and description of these patterns. The problem is formulated as one-class classification utilising a low dimensional subspace, where a novelty classifier is used to learn normal actions automatically and then to detect abnormal actions from low-level features extracted from a region of interest. The subspace is discovered (using both labelled and unlabelled data) by a locality preserving graph-based algorithm that utilises the Graph Laplacian of a specially designed parameter-less nearest neighbour graph. The methodology compares favourably with alternative subspace learning algorithms (both linear and non-linear) and direct one-class classification schemes commonly used for off-line abnormality detection in synthetic and real data. Based on these findings, the framework is extended to on-line abnormality detection in video sequences, utilising multiple independent detectors deployed over the image frame to learn the local normal patterns and infer abnormality for the complete scene. The method is compared with an alternative linear method to establish advantages and limitations in on-line abnormality detection scenarios. Analysis shows that the alternative approach is better suited for cases where the subspace learning is restricted on the labelled samples, while in the presence of additional unlabelled data the proposed approach using graph-based subspace learning is more appropriate

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately