Detecting abnormal human behaviour using multiple cameras

Antonakaki, Panagiota; Kosmopoulos, Dimitrios; Perantonis, Stavros

doi:10.1016/j.sigpro.2009.03.016

Cited by 70 publications

(39 citation statements)

References 31 publications

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“…The vision based input channels used in interactive systems include facial expression [15,16], gestures [17][18][19][20][21], head movement [22], the whole body action [23][24][25][26], eye gaze [27], and etc. Skin color [28,29], shape of the head, facial features [30,31] are often used to recognize and locate the faces.…”

Section: Input and Output Modalitiesmentioning

confidence: 99%

A Review of Multimodal Interaction

Liang¹,

Liang²,

Xue-qian³

et al. 2016

Advances in Computer Science Research

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Abstract-Due to the contribution to a more natural and efficient human-computer interaction style, multimodal interactive systems are becoming more and more commonplace and cover a wide range of applicants. Therefore, there seems to be an urgent need for an introduction to related concepts of multimodal interaction and an analytical review of the-state-of-the-art. In this paper, we try to present a brief review of some of the significant aspects in multimodal interaction. At first, the definition and advantages of multimodal systems are discussed, and then a brief development history of multimodal interaction is introduced. Next, we touch on the recent development in the breadth of work on input/output modalities and fusion engine. Finally, we provide a personal outlook for the research of multimodal interaction. It is our hope for this paper to provide a picture of the contemporary state of multimodal interaction research and a meaningful outlook for this research field.

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Section: Input and Output Modalitiesmentioning

confidence: 99%

A Review of Multimodal Interaction

Liang¹,

Liang²,

Xue-qian³

et al. 2016

Advances in Computer Science Research

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“…Interesting surveys could be found in [38] and [4]. The methods can be grouped in state models (e.g.…”

Section: Case Of Study: Individual Human Behaviour Analysismentioning

confidence: 99%

Constrained self-organizing feature map to preserve feature extraction topology

Azorín-López

Saval-Calvo

Fuster-Guilló

et al. 2016

Neural Comput & Applic

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In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-Dimensional constrained SelfOrganizing Map Preserving the Input Topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example

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“…Panagiota Antonakaki, Dimitrios Kosmopoulos, Stavros J. Perantonis etc. [12]proposed a human behavior understanding method based on bottom-up multiple camera system, using two different standards (the time length and the behavior of the track) to divid the behaviors into normal and abnormal behavior, this system using a support vector machine (SVM) identifier to identify the abnormal behavior.…”

Section: Introductionmentioning

confidence: 99%

A HMM-Based Structured Model for Human Behavior

Wang¹

2017

Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017

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Abstract.A structured recognition method of human behavior based on HMM is proposed to build the human action in this paper, only one camera which does not need to be adjust should be used in this approach. The robustness of the human behavior recognition model which is established by the free view angle of the camera is improved. The human behavior outline feature is segmented in video frame, some algorithm is used to establish the structure rob posture model and then to be standardized, at the last, the standardized rob postures of the same behavior are used to build the human body hidden Markov model (HMM) which has the independent position, independent translation, independent scale, independent rotating and the advantages of the free view angle, this model is verified by experiment has higher recognition rate.

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Detecting abnormal human behaviour using multiple cameras

Cited by 70 publications

References 31 publications

A Review of Multimodal Interaction

A Review of Multimodal Interaction

Constrained self-organizing feature map to preserve feature extraction topology

A HMM-Based Structured Model for Human Behavior

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