Results 1 - 10
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42
Anomaly detection in crowded scenes,”
- in IEEE Conference on Computer Vision and Pattern Recognition,
, 2010
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Sparse reconstruction cost for abnormal event detection
- In IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2011
"... We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the norm ..."
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Cited by 29 (3 self)
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We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. To condense the size of the dictionary, a novel dictionary selection method is designed with sparsity consistency constraint. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. Our method provides a unified solution to detect both local abnormal events (LAE) and global abnormal events (GAE). We further extend it to support online abnormal event detection by updating the dictionary incrementally. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our algorithm. 1.
Online Detection of Unusual Events in Videos via Dynamic Sparse Coding
"... Real-time unusual event detection in video stream has been a difficult challenge due to the lack of sufficient training information, volatility of the definitions for both normality and abnormality, time constraints, and statistical limitation of the fitness of any parametric models. We propose a fu ..."
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Cited by 19 (0 self)
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Real-time unusual event detection in video stream has been a difficult challenge due to the lack of sufficient training information, volatility of the definitions for both normality and abnormality, time constraints, and statistical limitation of the fitness of any parametric models. We propose a fully unsupervised dynamic sparse coding approach for detecting unusual events in videos based on online sparse reconstructibility of query signals from an atomically learned event dictionary, which forms a sparse coding bases. Based on an intuition that usual events in a video are more likely to be reconstructible from an event dictionary, whereas unusual events are not, our algorithm employs a principled convex optimization formulation that allows both a sparse reconstruction code, and an online dictionary to be jointly inferred and updated. Our algorithm is completely unsupervised, making no prior assumptions of what unusual events may look like and the settings of the cameras. The fact that the bases dictionary is updated in an online fashion as the algorithm observes more data, avoids any issues with concept drift. Experimental results on hours of real world surveillance video and several Youtube videos show that the proposed algorithm could reliably locate the unusual events in the video sequence, outperforming the current state-of-the-art methods. 1.
Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event driven processing
- ECCV
, 2010
"... This paper proposes a novel approach to anomalous behaviour detection in video. The approach is comprised of three key components. First, distributions of spatiotemporal oriented energy are used to model behaviour. This representation can capture a wide range of naturally occurring visual spacetime ..."
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Cited by 14 (6 self)
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This paper proposes a novel approach to anomalous behaviour detection in video. The approach is comprised of three key components. First, distributions of spatiotemporal oriented energy are used to model behaviour. This representation can capture a wide range of naturally occurring visual spacetime patterns and has not previously been applied to anomaly detection. Second, a novel method is proposed for comparing an automatically acquired model of normal behaviour with new observations. The method accounts for situations when only a subset of the model is present in the new observation, as when multiple activities are acceptable in a region yet only one is likely to be encountered at any given instant. Third, event driven processing is employed to automatically mark portions of the video stream that are most likely to contain deviations from the expected and thereby focus computational efforts. The approach has been implemented with real-time performance. Quantitative and qualitative empirical evaluation on a challenging set of natural image videos demonstrates the approach’s superior performance relative to various alternatives.
Incremental activity modelling in multiple disjoint cameras
- IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—Activity modeling and unusual event detection in a network of cameras is challenging, particularly when the camera views are not overlapped. We show that it is possible to detect unusual events in multiple disjoint cameras as context-incoherent patterns through incremental learning of time ..."
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Cited by 13 (7 self)
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Abstract—Activity modeling and unusual event detection in a network of cameras is challenging, particularly when the camera views are not overlapped. We show that it is possible to detect unusual events in multiple disjoint cameras as context-incoherent patterns through incremental learning of time delayed dependencies between distributed local activities observed within and across camera views. Specifically, we model multicamera activities using a Time Delayed Probabilistic Graphical Model (TD-PGM) with different nodes representing activities in different decomposed regions from different views and the directed links between nodes encoding their time delayed dependencies. To deal with visual context changes, we formulate a novel incremental learning method for modeling time delayed dependencies that change over time. We validate the effectiveness of the proposed approach using a synthetic data set and videos captured from a camera network installed at a busy underground station. Index Terms—Unusual event detection, multicamera activity modeling, time delay estimation, incremental structure learning. Ç 1
Video anomaly detection based on local statistical aggregates
- In Proceedings CVPR
, 2012
"... Anomalies in many video surveillance applications have local spatio-temporal signatures, namely, they occur over a small time window or a small spatial region. The dis-tinguishing feature of these scenarios is that outside this spatio-temporal anomalous region, activities appear nor-mal. We develop ..."
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Cited by 10 (0 self)
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Anomalies in many video surveillance applications have local spatio-temporal signatures, namely, they occur over a small time window or a small spatial region. The dis-tinguishing feature of these scenarios is that outside this spatio-temporal anomalous region, activities appear nor-mal. We develop a probabilistic framework to account for such local spatio-temporal anomalies. We show that our framework admits elegant characterization of optimal deci-sion rules. A key insight of the paper is that if anomalies are lo-cal optimal decision rules are local even when the nomi-nal behavior exhibits global spatial and temporal statisti-cal dependencies. This insight helps collapse the large am-bient data dimension for detecting local anomalies. Con-sequently, consistent data-driven local empirical rules with provable performance can be derived with limited training data. Our empirical rules are based on scores functions de-rived from local nearest neighbor distances. These rules ag-gregate statistics across spatio-temporal locations & scales, and produce a single composite score for video segments. We demonstrate the efficacy of our scheme on several video surveillance datasets and compare with existing work. 1.
Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture
- In Computer Vision and Pattern Recognition Workshops (CVPRW
, 2011
"... A robust and efficient anomaly detection technique is proposed, capable of dealing with crowded scenes where traditional tracking based approaches tend to fail. Initial foreground segmentation of the input frames confines the analysis to foreground objects and effectively ignores irrelevant backgrou ..."
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Cited by 10 (2 self)
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A robust and efficient anomaly detection technique is proposed, capable of dealing with crowded scenes where traditional tracking based approaches tend to fail. Initial foreground segmentation of the input frames confines the analysis to foreground objects and effectively ignores irrelevant background dynamics. Input frames are split into nonoverlapping cells, followed by extracting features based on motion, size and texture from each cell. Each feature type is independently analysed for the presence of an anomaly. Unlike most methods, a refined estimate of object motion is achieved by computing the optical flow of only the foreground pixels. The motion and size features are modelled by an approximated version of kernel density estimation, which is computationally efficient even for large training datasets. Texture features are modelled by an adaptively grown codebook, with the number of entries in the codebook selected in an online fashion. Experiments on the recently published UCSD Anomaly Detection dataset show that the proposed method obtains considerably better results than three recent approaches: MPPCA, social force, and mixture of dynamic textures (MDT). The proposed method is also several orders of magnitude faster than MDT, the next best performing method. 1.
Video parsing for abnormality detection
- In ICCV
, 2011
"... Detecting abnormalities in video is a challenging prob-lem since the class of all irregular objects and behaviors is infinite and thus no (or by far not enough) abnormal train-ing samples are available. Consequently, a standard set-ting is to find abnormalities without actually knowing what they are ..."
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Cited by 8 (1 self)
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Detecting abnormalities in video is a challenging prob-lem since the class of all irregular objects and behaviors is infinite and thus no (or by far not enough) abnormal train-ing samples are available. Consequently, a standard set-ting is to find abnormalities without actually knowing what they are because we have not been shown abnormal exam-ples during training. However, although the training data does not define what an abnormality looks like, the main paradigm in this field is to directly search for individual ab-normal local patches or image regions independent of an-other. To address this problem we parse video frames by estab-lishing a set of hypotheses that jointly explain all the fore-ground while, at same time, trying to find normal training samples that explain the hypotheses. Consequently, we can avoid a direct detection of abnormalities. They are discov-ered indirectly as those hypotheses which are needed for covering the foreground without finding an explanation by normal samples for themselves. We present a probabilistic model that localizes abnormalities using statistical infer-ence. On the challenging dataset of [15] it outperforms the state-of-the-art by 7 % to achieve a frame-based abnormal-ity classification performance of 91 % and the localization performance improves by 32 % to 76%. 1.
Stream-based Active Unusual Event Detection
"... Abstract. We present a new active learning approach to incorporate human feedback for on-line unusual event detection. In contrast to most existing unsupervised methods that perform passive mining for unusual events, our approach automatically requests supervision for critical points to resolve ambi ..."
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Cited by 7 (3 self)
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Abstract. We present a new active learning approach to incorporate human feedback for on-line unusual event detection. In contrast to most existing unsupervised methods that perform passive mining for unusual events, our approach automatically requests supervision for critical points to resolve ambiguities of interest, leading to more robust and accurate detection on subtle unusual events. The active learning strategy is formulated as a stream-based solution, i.e. it makes decision on-the-fly on whether to query for labels. It adaptively combines multiple active learning criteria to achieve (i) quick discovery of unknown event classes and (ii) refinement of classification boundary. Experimental results on busy public space videos show that with minimal human supervision, our approach outperforms existing supervised and unsupervised learning strategies in identifying unusual events. In addition, better performance is achieved by using adaptive multi-criteria approach compared to existing single criterion and multi-criteria active learning strategies. 1
