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Proceedings ArticleDOI

SEVA: A Salient Event Detection Framework from Video Shots Using Support Vector Data Description

TL;DR: This paper proposes SEVA (Salient Events for Video Analytics), a framework for accurate detection and localization of salient events from a given video shot based on statistical learning theory and models salient event detection as a one-class classification problem.
Abstract: In this paper we propose SEVA (Salient Events for Video Analytics), a framework for accurate detection and localization of salient events from a given video shot. Our proposed method is based on statistical learning theory and models salient event detection as a one-class classification problem. Video frames are split into blocks for extracting both the spatial and temporal features. Given a video shots we first track the moving foreground blob. Features are extracted using only the foreground pixels to avoid influence of the background. Using Support Vector Data Description (SVDD) in kernel feature space for each block in a given video frame, the decision boundary for the normal activity class is modeled. For a test video sequence, feature vectors are computed from the video frames and the learnt model is utilized to classify each block as normal or salient. Finally, we have adapted a spatio-temporal smoothing approach to remove the false positives. We have reported both qualitative and quantitative results of our experiments on two real-world benchmarked video datasets. Performance of SEVA is compared with five recent works on video event detection to validate its effectiveness.
References
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Journal ArticleDOI
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

46,906 citations

Journal ArticleDOI
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

9,627 citations


"SEVA: A Salient Event Detection Fra..." refers background in this paper

  • ...These salient events are also referred in literature as anomalies, outliers, surprises, peculiarities in different application domains [1]....

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  • ...Readers are requested to refer to the work reported in [1] for a detailed survey of related work....

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Book
01 Jan 2004
TL;DR: This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Abstract: Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

6,050 citations


Additional excerpts

  • ...The details of the individual feature extraction process is discussed below....

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Journal ArticleDOI
17 Jun 2006
TL;DR: A large-scale evaluation of an approach that represents images as distributions of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the χ2 distance.
Abstract: Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the ÷2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on 4 texture and 5 object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance.

1,863 citations

Journal ArticleDOI
TL;DR: Efficiency figures show that the proposed technique for motion detection outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate.
Abstract: This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.

1,777 citations


Additional excerpts

  • ...For a given frame ft, we denote an individual block of the grid at the ith row and jth column as Gt(i, j), where i = 1, 2, . . . , (H/S) and j = 1, 2, . . . , (W/S)....

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  • ...VI, concluding remarks and future research direction are given....

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