Detecting rare events in video using semantic primitives with HMM

Chan, M.; Hoogs, Anthony; Schmiederer, J.; Petersen, Matthew J.

doi:10.1109/icpr.2004.1333726

Cited by 51 publications

(46 citation statements)

References 10 publications

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“…The works relevant to the crowd scenes can be broadly divided into two categories, according to the type of scene representation adopted. One category is object-based approach, which considers the group as a collection of individuals [4][5] [6]. To understand crowd behavior, segmentation, detection, or tracking should be performed in advance.…”

Section: Related Workmentioning

confidence: 99%

Weighted Interaction Force Estimation for Abnormality Detection in Crowd Scenes

Zhu

Liu

Wang

et al. 2013

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a weighted interaction force estimation in the social force model(SFM)-based framework, in which the properties of surrounding individuals in terms of motion consistence, distance apart, and angle-of-view along moving directions are fully utilized in order to more precisely discriminate normal or abnormal behaviors of crowd. To avoid the challenges in object tracking in crowded videos, we first perform particle advection to capture the continuity of crowd flow and use these moving particles as individuals for the interaction force estimation. For a more reasonable interaction force estimation, we jointly consider the properties of surrounding individuals, assuming that the individuals with consistent motion (as a particle group) and the ones out of the angle-of-view have no influence on each other, besides the farther apart ones have weaker influence. In particular, particle groups are clustered by spectral clustering algorithm, in which a novel and high discriminative gait feature in frequency domain, combined with spatial and motion feature, is used. The estimated interaction forces are mapped to image span to form force flow, from which bag-of-word features are extracted. Sparse Topical Coding (STC) model is used to find abnormal events. Experiments conducted on three datasets demonstrate the promising performance of our work against other related ones.

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Section: Related Workmentioning

confidence: 99%

Weighted Interaction Force Estimation for Abnormality Detection in Crowd Scenes

Zhu

Liu

Wang

et al. 2013

Lecture Notes in Computer Science

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“…In (Hongeng et al, 2003) was described a complex event recognition method based on the definition of scenarios and on the use of SemiMarkov Chain (SMC). Chan et al proposed a method for detecting rare events by representing motions and space-time relations between objects using HMMs (Chan et al, 2004). A recognition method for group activities was defined in (Gong and Xiang, 2003) relying on DPN to model and detect actions involving multiple objects.…”

Section: Related Workmentioning

confidence: 99%

“…First methods considered point coordinates and local orientations on image trajectories as input features (Bashir et al, 2007;Buzan et al, 2004;Chan et al, 2004;Porikli, 2004). Using these features leads to express strict spatial similarity between trajectories.…”

Section: Related Workmentioning

confidence: 99%

Video Event Classification and Detection Using 2d Trajectories

Hervieu¹,

Bouthémy²,

Cadre

2008

Proceedings of the Third International Conference on Computer Vision Theory and Applications

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Abstract:This paper describes an original statistical trajectory-based approach which can address several issues related to dynamic video content understanding: unsupervised clustering of events, recognition of events corresponding to learnt classes of dynamic video contents, and detection of unexpected events. Appropriate local differential features combining curvature and motion magnitude are robustly computed on the trajectories. They are invariant to image translation, in-the-plane rotation and scale transformation. The temporal causality of these features is then captured by hidden Markov models whose states are properly quantized values, and similarity between trajectories is expressed by exploiting the HMM framework. We report experiments on two sets of data, a first one composed of typical classes of synthetic (noised) trajectories (such as parabola or clothoid), and a second one formed with trajectories computed in sports videos. We have also favorably compared our method to other ones, including feature histogram comparison, use of the longest common subsequence (LCSS) distance and SVM-based classification.

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“…approach. The transition of the semantic primitives are modeled by HMMs (Chan et al, 2004). Because of the early symbolic abstraction of trajectory information, this method can only be applied in a restricted scenario.…”

Section: Recognition Of Human Movements Activities and Actionsmentioning

confidence: 99%