28 research outputs found

    Online video-based abnormal detection using highly motion techniques and statistical measures

    Get PDF
    At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for he accuracy while attaining simultaneously low values for the processing time

    Video anomaly detection with compact feature sets for online performance

    Get PDF
    Over the past decade, video anomaly detection has been explored with remarkable results. However, research on methodologies suitable for online performance is still very limited. In this paper, we present an online framework for video anomaly detection. The key aspect of our framework is a compact set of highly descriptive features, which is extracted from a novel cell structure that helps to define support regions in a coarse-to-fine fashion. Based on the scene's activity, only a limited number of support regions are processed, thus limiting the size of the feature set. Specifically, we use foreground occupancy and optical flow features. The framework uses an inference mechanism that evaluates the compact feature set via Gaussian Mixture Models, Markov Chains, and Bag-of-Words in order to detect abnormal events. Our framework also considers the joint response of the models in the local spatio-temporal neighborhood to increase detection accuracy. We test our framework on popular existing data sets and on a new data set comprising a wide variety of realistic videos captured by surveillance cameras. This particular data set includes surveillance videos depicting criminal activities, car accidents, and other dangerous situations. Evaluation results show that our framework outperforms other online methods and attains a very competitive detection performance compared with state-of-the-art non-online methods

    Abnormal behavior detection using sparse representations through sequential generalization of k-means

    Get PDF
    The potential capability to automatically detect and classify human behavior as either normal or abnormal events is an important aspect in intelligent monitoring/surveillance systems. This study presents a new high-performance framework for detecting behavioral abnormalities in video streams by utilizing only the patterns for normal behaviors. In this paper, we used a hybrid descriptor, called a foreground optical flow energy (FGOFE), which makes use of two effective motion techniques in order to extract the most descriptive spatiotemporal features in video sequences. The FGOFE descriptor can effectively capture both weak and sudden incidents in a scene. The sequential generalization of k-means (SGK) algorithm was applied in this study to generate the dictionary set that can sparsely represent each signal; in addition, the orthogonal matching pursuit algorithm was utilized to recover high-dimensional sparse features when referring to a few numbers of noisy linear measurements. Using the SGK allows gaining a less complex and quicker implementation compared to other dictionary learning methods. We conducted comprehensive experiments to analyze and evaluate the ability of our framework in detecting abnormalities using several public benchmarks, which contain different abnormal samples and various contextual compositions. The experimental results show that the proposed framework achieved high detection accuracy (up to 95.33%) and low frame processing time (31 ms on average) compared to the relevant related work

    Algorithms for anomaly detection in video sequences through discriminative models

    Full text link
    Monitoring public areas with pedestrians is a task that has to be frequently accomplished by means of security systems. Nevertheless, manual detection of these anomalies is a tough task and it is easy to lose interesting events when many areas have to be attended. This is the main reason why the automated detection of these anomalies and interesting events in general has become an important source of research in the past years, specially in the eld of computer vision. Automated anomaly detection is still an open task even though that many methods have been proposed. One of the reasons is that a successful and accurate anomaly detection algorithm strongly depends on the context and the de nition of the anomalies to detect and the objects that produce them. The state of the art included in this work has been developed to make a complete study of all these aspects in detail, as well as a study of advantages and drawbacks of the main methods of the literature, helping to choose the best techniques and strategies for speci c surveillance scenarios. Since there is a great di culty to model every anomaly, we have decided to fashion the normality by means of Gaussian mixture models, which are relatively simple methods compared to others in the literature such as [1, 2], but that have shown potential at detecting anomalies. This can be observed on the methods proposed in [3] and [4]. We have decided to work at pixel level. Thus, to feed the model, discriminative descriptors are built based on a robust optical ow method, that has become the main source of motion and textural information of the scene. This fact makes this work di erent to other state-of-the-art approaches that work at pixel-level, whose optical ow is not capable to give such a detailed information of the scene. Finally, the evaluation of the nal algorithm is performed exhaustively from a baseline method, whose descriptor grows depending on the best results so far on a publicly available dataset. Detection results are compared with the state-of-the-art methods, concluding that our method is at the same level of the methods proposed in the literature

    Video anomaly detection using deep generative models

    Full text link
    Video anomaly detection faces three challenges: a) no explicit definition of abnormality; b) scarce labelled data and c) dependence on hand-crafted features. This thesis introduces novel detection systems using unsupervised generative models, which can address the first two challenges. By working directly on raw pixels, they also bypass the last

    Essentials of Business Analytics

    Get PDF

    Spatiotemporal enabled Content-based Image Retrieval

    Full text link

    Applications

    Get PDF
    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

    Get PDF
    When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available
    corecore