Activity Analysis Using Spatio-Temporal Trajectory Volumes in Surveillance Applications

Janoos, Firdaus; Singh, Shweta; Irfanoglu, Okan; Machiraju, Raghu; Parent, Rick

doi:10.1109/vast.2007.4388990

Cited by 16 publications

(8 citation statements)

References 18 publications

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“…In [AA08a] they propose a method for spatio‐temporal aggregation of traffic, which allows the user to explore the usage of a road network at various times simultaneously. Pedestrian trajectories are analyzed in [JFSe07] to find anomalies using a wavelet approach.…”

Section: Related Workmentioning

confidence: 99%

Visualization of vessel movements

Willems

Wetering

Wijk

2009

Computer Graphics Forum

169

107

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We propose a geographical visualization to support operators of coastal surveillance systems and decision making analysts to get insights in vessel movements. For a possibly unknown area, they want to know where significant maritime areas, like highways and anchoring zones, are located. We show these features as an overlay on a map. As source data we use AIS data: Many vessels are currently equipped with advanced GPS devices that frequently sample the state of the vessels and broadcast them. Our visualization is based on density fields that are derived from convolution of the dynamic vessel positions with a kernel. The density fields are shown as illuminated height maps. Combination of two fields, with a large and small kernel provides overview and detail. A large kernel provides an overview of area usage revealing vessel highways. Details of speed variations of individual vessels are shown with a small kernel, highlighting anchoring zones where multiple vessels stop. Besides for maritime applications we expect that this approach is useful for the visualization of moving object data in general.

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Section: Related Workmentioning

confidence: 99%

Visualization of vessel movements

Willems

Wetering

Wijk

2009

Computer Graphics Forum

169

107

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“…Another pattern is the relative motion between objects, which is emphasized in a visualization proposed by Crnovrsanin et al [37], where abstract representations may solve possible confusions when movements are only displayed spatially. Anomalous behaving objects can be found by means of wavelets [85] as demonstrated with pedestrian data or with a combination of machine learning techniques and visual analytic tools as shown by Liao et al [97] with trajectories of taxis. Apart from historical data analysis, there is a range of visualizations for real-time traffic, such as for air traffic control [19].…”

Section: Trajectory Visualizationsmentioning

confidence: 99%

Visualization of Vessel Traffic

Willems

Scheepens

Wetering

et al. 2013

Situation Awareness With Systems of Systems

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“…Papers recognizing this are currently emerging. Janoos et al [9] used a visual analytics approach to learn models of pedestrian motion patterns from video surveillance data, in order to distinguish typical from unusual behavior in order to flag security breaches in outdoor environments. Their semi-supervised learning approach in which users interact with video stream data improves upon the standard unsupervised learning schemes that are typically used in these scenarios.…”

Section: Related Workmentioning

confidence: 99%

A visual analytics approach to model learning

Garg

Ramakrishnan

Mueller

2010

2010 IEEE Symposium on Visual Analytics Science and Technology

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The process of learning models from raw data typically requires a substantial amount of user input during the model initialization phase. We present an assistive visualization system which greatly reduces the load on the users and makes the process of model initialization and refinement more efficient, problem-driven, and engaging. Utilizing a sequence segmentation task with a Hidden Markov Model as an example, we assign each token in the sequence a feature vector based on its various properties within the sequence. These vectors are then clustered according to similarity, generating a layout of the individual tokens in form of a node link diagram where the length of the links is determined by the feature vector similarity. Users may then tune the weights of the feature vector components to improve the segmentation, which is visualized as a better separation of the clusters. Also, as individual clusters represent different classes, the user can now work at the cluster level to define token classes, instead of labelling one entry at time. Inconsistent entries visually identify themselves by locating at the periphery of clusters, and the user then helps refine the model by resolving these inconsistencies. Our system therefore makes efficient use of the knowledge of its users, only requesting user assistance for non-trivial data items. It so allows users to visually analyze data at a higher, more abstract level, improving scalability. INTRODUCTIONWith the tremendous growth in physical and online data collection technology, we are now experiencing an explosion of digital information. Since a large amount of these data are unstructured, various machine learning techniques have been developed to assign structure to these data to make them machine readable. This process can allow the machine to reason with and draw insight from data almost automatically. However, all such tasks depend heavily on large amounts of user-tagged data as the starting point, and use various semi-supervised learning methods [19]. Due to the high user input required, such tagged data is difficult to construct. Further, data is dynamic, and as a dataset grows and changes, we might need to supplement the tagged data from time to time. We propose to make this task simpler and interactive by designing a system where the user can obtain a visual overview of the dataset, and in that visual interface only tags those data elements that the system cannot easily resolve itself.One crucial idea behind our system is that given good feature vectors to represent each data point, points that are similar will be close-by in the feature vector space. Here, we mean data-points which though rich in semantics, do not have an explicit highdimensional feature vector automatically attached to them. In such cases we need to design feature vectors to represent the semantics and structure of the data-points. We aim to achieve this in our system by designing feature vectors which encompass a data point's structure, context, and location in the dataset. If some s...

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Activity Analysis Using Spatio-Temporal Trajectory Volumes in Surveillance Applications

Cited by 16 publications

References 18 publications

Visualization of vessel movements

Visualization of vessel movements

Visualization of Vessel Traffic

A visual analytics approach to model learning

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