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Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition. [PDF]
Sensors (Basel)Varga D.
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Action Recognition using HighLevel Action Units
International Journal on Recent and Innovation Trends in Computing and Communication, 2015 openaire +1 more source
InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities. [PDF]
PLoS OneJadoon YK +6 more
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Actionness-Assisted Recognition of Actions
2015 IEEE International Conference on Computer Vision (ICCV), 2015We elicit from a fundamental definition of action low-level attributes that can reveal agency and intentionality. These descriptors are mainly trajectory-based, measuring sudden changes, temporal synchrony, and repetitiveness. The actionness map can be used to localize actions in a way that is generic across action and agent types. Furthermore, it also
Ye Luo, Loong-Fah Cheong, An Tran
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2020
Our brain is a superfast action recognition system that’s hard to match. In terms of deep learning, our brain routinely does many things to recognize actions, and it works fast! In this chapter we cover practical methods and tools for video action recognition and classification.
Aytakin Nabisoy, Malekzadeh, Saber
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Our brain is a superfast action recognition system that’s hard to match. In terms of deep learning, our brain routinely does many things to recognize actions, and it works fast! In this chapter we cover practical methods and tools for video action recognition and classification.
Aytakin Nabisoy, Malekzadeh, Saber
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Action recognition through discovering distinctive action parts
Journal of the Optical Society of America A, 2015Recent methods based on midlevel visual concepts have shown promising capabilities in the human action recognition field. Automatically discovering semantic entities such as action parts remains challenging. In this paper, we present a method of automatically discovering distinctive midlevel action parts from video for recognition of human actions.
Feifei, Chen +4 more
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2010
Action recognition is one of the most active research fields in computer vision. This chapter first reviews the action recognition methods in literature from two aspects: action representation and recognition strategy. Then, a novel method for classifying human actions from image sequences is investigated.
Qingdi Wei, Xiaoqin Zhang, Weiming Hu
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Action recognition is one of the most active research fields in computer vision. This chapter first reviews the action recognition methods in literature from two aspects: action representation and recognition strategy. Then, a novel method for classifying human actions from image sequences is investigated.
Qingdi Wei, Xiaoqin Zhang, Weiming Hu
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Self-recognition: body and action
Trends in Cognitive Sciences, 2002How do I know it was me who moved? A recent experimental study illustrates the contribution of one's body schema and awareness of one's own actions to self-recognition. The results provide evidence that bodily cues and action cues are important in self-recognition, and they demonstrate that action cues overrule bodily cues.
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2011
Methods for analyzing humans and their actions from monocular or multi-view video data are required in many different applications. In this chapter simple LBP-based approaches for action recognition are introduced. The methods perform very favorably compared to the state-of-the-art for test video sequences commonly used in the research community.
Matti Pietikäinen +3 more
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Methods for analyzing humans and their actions from monocular or multi-view video data are required in many different applications. In this chapter simple LBP-based approaches for action recognition are introduced. The methods perform very favorably compared to the state-of-the-art for test video sequences commonly used in the research community.
Matti Pietikäinen +3 more
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Cross-Domain Human Action Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012Conventional human action recognition algorithms cannot work well when the amount of training videos is insufficient. We solve this problem by proposing a transfer topic model (TTM), which utilizes information extracted from videos in the auxiliary domain to assist recognition tasks in the target domain. The TTM is well characterized by two aspects: 1)
Wei, Bian, Dacheng, Tao, Yong, Rui
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