Detecting anomalies in people’s trajectories using spectral graph analysis

Calderara, Simone; Heinemann, Uri; Prati, Andrea; Cucchiara, Rita; Tishby, Naftali

doi:10.1016/j.cviu.2011.03.003

Cited by 87 publications

(41 citation statements)

References 44 publications

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“…Cheriyadat et al [11] tracks low level features using optical flow and those feature trajectories were clustered to find dominant motion. Calderara et al [12] proposed to track each person in a crowd and to extract their trajectories to represent those as sequences of transitions between nodes in a graph and they developed invariant distance measures to detect anomalous trajectories.…”

Section: Related Workmentioning

confidence: 99%

An efficient method for crowd event recognition based on motion patterns

Sindhuja¹,

Srinivasagan²,

Kalaiselvi³

2014

2014 International Conference on Recent Trends in Information Technology

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An automated visual monitoring process expands from low level analysis of object detection and tracking to the interpretation of their behaviors. Analyzing human crowd is an emerging trend in intelligent video surveillance for the purpose of detecting abnormalities. Tracking every human being in a crowd and analyzing their behavior is a challenging task due to occlusions. Hence, the crowd can be handled as a group entity instead of tracking the individual in the crowd. The behavior of the crowd can be distinguished with motion patterns due to prominent spatio-temporal characteristics. The proposed system involves a systematic approach to recognize the global events in human crowd through observing motion patterns such as flow, speed and direction. Initially as a preprocessing step, background subtraction is performed to extract the foreground blobs and optical flow is estimated to obtain the velocity and direction of motion. The human crowds are then clustered based on similar direction and proximity using Adjacency Matrix based Clustering (AMC). After clustering, the centroid and orientation of the cluster are extracted inorder to represent the behavior of crowd. Finally the multiclass Support Vector Machine (SVM) is trained to correctly recognize the behavior of crowd. Keywords-crowd behavior, optical flow, Adjacency Matrix based Clustering, Multiclass SVM.I.

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Section: Related Workmentioning

confidence: 99%

An efficient method for crowd event recognition based on motion patterns

Sindhuja¹,

Srinivasagan²,

Kalaiselvi³

2014

2014 International Conference on Recent Trends in Information Technology

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“…In the literature, one category of the existing approaches is based on trajectory, such as [17][18][19][20][21]. These methods analyze trajectories, which are obtained via tracing amorphous blobs through consecutive video frames, constructing a normal motion model and detecting any trajectories with major deviation from the learned model as anomalous.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Spatio-temporal context analysis within video volumes for anomalous-event detection and localization

Wang

et al. 2015

Neurocomputing

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“…We can obtain abnormalities [5] from the reconstructed document by using the distance measure proposed in [10]. However, this measure does not take into account the spatial locality of the anomaly.…”

Section: Localized Abnormality Measurementioning

confidence: 99%

Localized anomaly detection via hierarchical integrated activity discovery

Chockalingam

Emonet

Odobez

2013

2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance

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LOCALIZED ANOMALY DETECTION VIA HIERARCHICAL INTEGRATED ACTIVITY DISCOVERYWith the increasing number and variety of camera installations, unsupervised methods that learn typical activities have become popular for anomaly detection. In this thesis, we consider recent methods based on temporal probabilistic models and improve them in multiple ways. Our contributions are the following: (i) we integrate the low level processing and the temporal activity modeling, showing how this feedback improves the overall quality of the captured information, (ii) we show how the same approach can be taken to do hierarchical multi-camera processing, (iii) we use spatial analysis of the anomalies both to perform local anomaly detection and to frame automatically the detected anomalies. We illustrate the approach on both traffic data and videos coming from a metro station. We also investigate the application of topic models in Brain Computing Interfaces for Mental Task classification. We observe a classification accuracy of up to 68% for four Mental Tasks on individual subjects.ii ACKNOWLEDGMENTS This thesis is a culmination of inspiration and knowledge obtained during my collaboration with research groups at CSU and Idiap Research Institute.

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Detecting anomalies in people’s trajectories using spectral graph analysis

Cited by 87 publications

References 44 publications

An efficient method for crowd event recognition based on motion patterns

An efficient method for crowd event recognition based on motion patterns

Spatio-temporal context analysis within video volumes for anomalous-event detection and localization

Localized anomaly detection via hierarchical integrated activity discovery

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