Learning to recognize human action sequences

2003 ◽  
Author(s):  
Chen Yu ◽  
D.H. Ballard
Keyword(s):  
Human Action ◽  
Action Sequences

Human Action Recognition Algorithm Based on Key Posture

2013 ◽  
Vol 631-632 ◽  
pp. 1303-1308
Author(s):  
He Jin Yuan
Keyword(s):  
Action Recognition ◽  
Clustering Algorithm ◽  
Recognition Accuracy ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Training Samples ◽  
Action Sequences

A novel human action recognition algorithm based on key posture is proposed in this paper. In the method, the mesh features of each image in human action sequences are firstly calculated; then the key postures of the human mesh features are generated through k-medoids clustering algorithm; and the motion sequences are thus represented as vectors of key postures. The component of the vector is the occurrence number of the corresponding posture included in the action. For human action recognition, the observed action is firstly changed into key posture vector; then the correlevant coefficients to the training samples are calculated and the action which best matches the observed sequence is chosen as the final category. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition. The average recognition accuracy can exceed 90%.

scholarly journals Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

2020 ◽  
Vol 34 (07) ◽  
pp. 12281-12288
Author(s):  
Zhenyi Wang ◽  
Ping Yu ◽  
Yang Zhao ◽  
Ruiyi Zhang ◽  
Yufan Zhou ◽  
...  
Keyword(s):  
Human Action ◽  
Human Motion ◽  
Motion Generation ◽  
Action Sequence ◽  
Sequence Generation ◽  
Latent Space ◽  
Action Sequences ◽  
Mixed Classes ◽  
Standard Action ◽  
Net Framework

Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are generated by a frame-wise decoder shared by all latent action-poses. Specifically, an implicit RNN is defined to model smooth latent sequences, whose randomness (diversity) is controlled by noise from the input. Different from standard action-prediction methods, our model can generate action sequences from pure noise without any conditional action poses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which can not only generate diverse action sequences of a particular class or mix classes, but also learns to classify action sequences within the same model. Experimental results show the superiority of our method in both diverse action-sequence generation and classification, relative to existing methods.

scholarly journals Temporal-order judgement task suggests chronological action representations in motor experts and non-experts

2020 ◽  
Vol 73 (11) ◽  
pp. 1879-1890
Author(s):  
Róisín Elaine Harrison ◽  
Martin Giesel ◽  
Constanze Hesse
Keyword(s):  
Temporal Order ◽  
Human Action ◽  
Natural Order ◽  
Action Sequence ◽  
Motor Expertise ◽  
Motor Priming ◽  
Human Movements ◽  
Action Sequences ◽  
Temporal Order Judgement ◽  
Tentative Evidence

Motor priming studies have suggested that human movements are mentally represented in the order in which they usually occur (i.e., chronologically). In this study, we investigated whether we could find evidence for these chronological representations using a paradigm which has frequently been employed to reveal biases in the perceived temporal order of events—the temporal-order judgement task. We used scrambled and unscrambled images of early and late movement phases from an everyday action sequence (“stepping”) and an expert action sequence (“sprinting”) to examine whether participants’ mental representations of actions would bias their temporal-order judgements. In addition, we explored whether motor expertise mediated the size of temporal-order judgement biases by comparing the performances of sprinting experts with those of non-experts. For both action types, we found significant temporal-order judgement biases for all participants, indicating that there was a tendency to perceive images of human action sequences in their natural order, independent of motor expertise. Although there was no clear evidence that sprinting experts showed larger biases for sprinting action sequences than non-experts, considering sports expertise in a broader sense provided some tentative evidence for the idea that temporal-order judgement biases may be mediated by more general motor and/or perceptual familiarity with the running action rather than specific motor expertise.

Understanding Social Causalities Behind Human Action Sequences

2017 ◽  
Vol 28 (8) ◽  
pp. 1801-1813 ◽  
Author(s):  
Ruichu Cai ◽  
Zhenjie Zhang ◽  
Zhifeng Hao ◽  
Marianne Winslett
Keyword(s):  
Human Action ◽  
Action Sequences

scholarly journals Learning non-adjacent rules and non-adjacent dependencies from human actions in 9-month-old infants

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252959
Author(s):  
Helen Shiyang Lu ◽  
Toben H. Mintz
Keyword(s):  
Language Acquisition ◽  
Rule Learning ◽  
Human Action ◽  
Human Actions ◽  
Action Sequences ◽  
Visual Domain

Seven month old infants can learn simple repetition patterns, such as we-fo-we, and generalize the rules to sequences of new syllables, such as ga-ti-ga. However, repetition rule learning in visual sequences seems more challenging, leading some researchers to claim that this type of rule learning applies preferentially to communicative stimuli. Here we demonstrate that 9-month-old infants can learn repetition rules in sequences of non-communicative dynamic human actions. We also show that when primed with these non-adjacent repetition patterns, infants can learn non-adjacent dependencies that involve memorizing the dependencies between specific human actions—patterns that prior research has shown to be difficult for infants in the visual domain and in speech. We discuss several possible mechanisms that account for the apparent advantage stimuli involving human action sequences has over other kinds of stimuli in supporting non-adjacent dependency learning. We also discuss possible implications for theories of language acquisition.

RECOGNIZING OVERLAPPED HUMAN ACTIVITIES FROM A SEQUENCE OF PRIMITIVE ACTIONS VIA DELETED INTERPOLATION

2008 ◽  
Vol 22 (07) ◽  
pp. 1343-1362
Author(s):  
KRIS M. KITANI ◽  
YOICHI SATO ◽  
AKIHIRO SUGIMOTO
Keyword(s):  
Human Activity ◽  
Domain Knowledge ◽  
A Priori ◽  
Human Action Recognition ◽  
Human Action ◽  
Knowledge Based ◽  
Action Sequences ◽  
Hierarchical Nature ◽  
High Level ◽  
Context Free

The high-level recognition of human activity requires a priori hierarchical domain knowledge as well as a means of reasoning based on that knowledge. Based on insights from perceptual psychology, the problem of human action recognition is approached on the understanding that activities are hierarchical, temporally constrained and at times temporally overlapped. A hierarchical Bayesian network (HBN) based on a stochastic context-free grammar (SCFG) is implemented to address the hierarchical nature of human activity recognition. Then it is shown how the HBN is applied to different substrings in a sequence of primitive action symbols via deleted interpolation (DI) to recognize temporally overlapped activities. Results from the analysis of action sequences based on video surveillance data show the validity of the approach.

scholarly journals A suppression hierarchy among competing motor programs drives sequential grooming in Drosophila

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Andrew M Seeds ◽  
Primoz Ravbar ◽  
Phuong Chung ◽  
Stefanie Hampel ◽  
Frank M Midgley ◽  
...  
Keyword(s):  
Motor Program ◽  
Body Part ◽  
Human Action ◽  
Body Parts ◽  
Motor Sequence ◽  
Sensory Drive ◽  
Motor Programs ◽  
Action Sequences ◽  
Organizational Principles ◽  
Individual Body

Motor sequences are formed through the serial execution of different movements, but how nervous systems implement this process remains largely unknown. We determined the organizational principles governing how dirty fruit flies groom their bodies with sequential movements. Using genetically targeted activation of neural subsets, we drove distinct motor programs that clean individual body parts. This enabled competition experiments revealing that the motor programs are organized into a suppression hierarchy; motor programs that occur first suppress those that occur later. Cleaning one body part reduces the sensory drive to its motor program, which relieves suppression of the next movement, allowing the grooming sequence to progress down the hierarchy. A model featuring independently evoked cleaning movements activated in parallel, but selected serially through hierarchical suppression, was successful in reproducing the grooming sequence. This provides the first example of an innate motor sequence implemented by the prevailing model for generating human action sequences.

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