Bidirectional integrated random fields for human behaviour understanding

Liu, A.A.

doi:10.1049/el.2011.3530

Cited by 10 publications

(6 citation statements)

References 2 publications

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“…This method requires reliable detection of body joints and reliable tracking, which are difficult to achieve in computer vision today. Further, several other methods [6,35,19,17,34] were proposed that applied 3D reconstruction for multi-view action recognition. However, 3D reconstruction or 3D structure feature requires a reliable depth camera and strict coordinate of views, which are computationally expensive.…”

Section: Feature Representationmentioning

confidence: 99%

Human action recognition via multi-task learning base on spatial–temporal feature

Guo

Chen

2015

Information Sciences

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Section: Feature Representationmentioning

confidence: 99%

Human action recognition via multi-task learning base on spatial–temporal feature

Guo

Chen

2015

Information Sciences

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“…Some leading representations include learned geometrical models of the human body parts [13], space-time pattern templates [6,14], shape or form features [15][16][17], sequential model [18][19][20][21][22], interest point based representations [23,5], and motion/optical flow patterns [24].…”

Section: Related Workmentioning

confidence: 99%

Human Action Recognition Bases on Local Action Attributes

Zhang¹,

Lin²,

Nie³

et al. 2015

Journal of Electrical Engineering and Technology

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-Human action recognition received many interest in the computer vision community.Most of the existing methods focus on either construct robust descriptor from the temporal domain, or computational method to exploit the discriminative power of the descriptor. In this paper we explore the idea of using local action attributes to form an action descriptor, where an action is no longer characterized with the motion changes in the temporal domain but the local semantic description of the action. We propose an novel framework where introduces local action attributes to represent an action for the final human action categorization. The local action attributes are defined for each body part which are independent from the global action. The resulting attribute descriptor is used to jointly model human action to achieve robust performance. In addition, we conduct some study on the impact of using body local and global low-level feature for the aforementioned attributes. Experiments on the KTH dataset and the MV-TJU dataset show that our local action attribute based descriptor improve action recognition performance.

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“…Morency et al proposed latent-dynamic conditional random field to capture both inter-class and intra-class dynamics during human actions [33]. Recently, Liu et al proposed a bidirectional-integrated random fields model for this task [36]. Different from the random fields models above, which model human action sequence in one shot, this model leverages CRF for sequence segmentation and HCRF for sequence classification and then bridge both by modifying the feature functions to propagate sequence classification or segmentation information in-between.…”

Section: Introductionmentioning

confidence: 99%