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Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation [article]

Mohammad Sadegh Aliakbarian, Fatemehsadat Saleh, Basura Fernando, Mathieu Salzmann, Lars Petersson, Lars Andersson
2016 arXiv   pre-print
We outperform the state-of-the-art methods that, as us, rely only on RGB frames as input for both action recognition and anticipation.  ...  Action recognition and anticipation are key to the success of many computer vision applications.  ...  Our Method Our goal is to leverage both context-aware and actionaware features for action recognition and anticipation.  ... 
arXiv:1611.05520v2 fatcat:lrqzm6lllbbhvn2wwb2hsp6ut4

ADMT: Advanced Driver's Movement Tracking system using Spatio-temporal interest points and maneuver anticipation using deep neural networks

Shilpa Gite, Biswajeet Pradhan, Abdullah Alamri, Ketan Kotecha
2021 IEEE Access  
MACHINE LEARNING AND DEEP LEARNING FOR ACTION RECOGNITION For action recognition problems, probabilistic approaches were used in the recent past, whereas recent developments are executed mainly by machine  ...  Owing to multiple action sequences, automated moderation of driver activity anticipation is challenging for these techniques [8] .  ... 
doi:10.1109/access.2021.3096032 fatcat:mcuy36ydyfaynp5itq6ft6txvi

Predicting the Future: A Jointly Learnt Model for Action Anticipation

Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes
2019 2019 IEEE/CVF International Conference on Computer Vision (ICCV)  
Then, the loss for the final model that learns the context descriptor C t and is reinforced by both deep future sequence synthesisers (GAN models), and the future action classification can be written as  ...  Hence context descriptor learning is influenced by both the future representation prediction, and the action anticipation task. of GAN approaches can be found for human action recognition [1, 33] .  ... 
doi:10.1109/iccv.2019.00566 dblp:conf/iccv/GammulleDSF19 fatcat:w2jhqfwgb5hvthtgtgcccgl2he

Encouraging LSTMs to Anticipate Actions Very Early [article]

Mohammad Sadegh Aliakbarian, Fatemeh Sadat Saleh, Mathieu Salzmann, Basura Fernando, Lars Petersson, Lars Andersson
2017 arXiv   pre-print
To this end, we develop a multi-stage LSTM architecture that leverages context-aware and action-aware features, and introduce a novel loss function that encourages the model to predict the correct class  ...  In contrast to the widely studied problem of recognizing an action given a complete sequence, action anticipation aims to identify the action from only partially available videos.  ...  Authors thank Oscar Friberg for his assistance in conducting additional experiments of the supplementary material.  ... 
arXiv:1703.07023v3 fatcat:izbs6p56uzeilnesiii75wbfqy

Predicting the Future: A Jointly Learnt Model for Action Anticipation [article]

Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes
2019 arXiv   pre-print
Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting both the visual and temporal  ...  In contrast to current state-of-the-art methods which first learn a model to predict future video features and then perform action anticipation using these features, the proposed framework jointly learns  ...  Then, the loss for the final model that learns the context descriptor C t and is reinforced by both deep future sequence synthesisers (GAN models), and the future action classification can be written as  ... 
arXiv:1912.07148v1 fatcat:6gdvbgfttzeaniiqzmhbwkrayy

Designing a Deep Neural Networks Structure to Acquire the Top Level Depiction of Human Interactions

2019 International Journal of Engineering and Advanced Technology  
We design a deep neural network structure to acquire the top-level depiction of human activity combining both activity attributes as well as context features.  ...  info, spatiotemporal interest point-based approaches, and also human strolling proposition recognition Nevertheless, there has actually really been no methodical research study of individual action recognition  ...  As the look sequence and also optical flow series are relatively simple to acquire, a lot of deep learning approaches embrace the appeal collection and optical circulation collection as their input, with  ... 
doi:10.35940/ijeat.a1172.109119 fatcat:u27dkjlnfrckba3uybg2akqeua

Deep Learning for Assistive Computer Vision [chapter]

Marco Leo, Antonino Furnari, Gerard G. Medioni, Mohan Trivedi, Giovanni M. Farinella
2019 Lecture Notes in Computer Science  
achieved in five main areas, namely, object classification and localization, scene understanding, human pose estimation and tracking, action/event recognition and anticipation.  ...  The paper is concluded with a discussion and insights for future directions.  ...  Action and event recognition The action recognition task is related to the identification of the different possible actions performed by a human from a sequence of frames, where the actions may or may  ... 
doi:10.1007/978-3-030-11024-6_1 fatcat:ehmobmgjcba2tkopvfdfxhyb4i

Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components

Shafina Bibi, Nadeem Anjum, Tehmina Amjad, Graeme McRobbie, Naeem Ramzan
2020 IEEE Access  
RELATED WORK This section reviews the state-of-the art approaches in the context of human action recognition/ anticipation. A.  ...  This approach uses a global regularizer to learn hidden features and a temporal aware cross entropy to address the challenges of diverse motion in an action sequence captured from single view camera.  ...  His paper was awarded best paper award 2017 of IEEE Transaction of Circuit and System for Video Technology and number of conference papers were selected for best student paper award.  ... 
doi:10.1109/access.2020.3012557 fatcat:rtbug4abybe7vpuqxu233bk5xe

Learning to Anticipate Future with Dynamic Context Removal [article]

Xinyu Xu, Yong-Lu Li, Cewu Lu
2022 arXiv   pre-print
Anticipating future events is an essential feature for intelligent systems and embodied AI.  ...  However, compared to the traditional recognition task, the uncertainty of future and reasoning ability requirement make the anticipation task very challenging and far beyond solved.  ...  Acknowledgement We appreciate the support from National Natural Science Foundation of China (No.72192821, 72192820), Shanghai Municipal Science and Technology Major Project  ... 
arXiv:2204.02587v2 fatcat:ne7uwvgztzhwrm4ojkd4wrbvua

Encouraging LSTMs to Anticipate Actions Very Early

Mohammad Sadegh Aliakbarian, Fatemeh Sadat Saleh, Mathieu Salzmann, Basura Fernando, Lars Petersson, Lars Andersson
2017 2017 IEEE International Conference on Computer Vision (ICCV)  
action recognition and anticipation.  ...  While the main paper discusses action anticipation, here, we focus on evaluating our approach on the task of action recognition.  ...  Authors thank Oscar Friberg for his assistance in conducting additional experiments of the supplementary material.  ... 
doi:10.1109/iccv.2017.39 dblp:conf/iccv/AkbarianSSFPA17 fatcat:l7xdassusjaqtinbhro7kl77ve

Action Anticipation By Predicting Future Dynamic Images [article]

Cristian Rodriguez, Basura Fernando, Hongdong Li
2018 arXiv   pre-print
In this paper, we present a method for human action anticipation by predicting the most plausible future human motion.  ...  Our method outperforms the currently best performing action-anticipation methods by 4% on JHMDB-21, 5.2% on UT-Interaction and 5.1% on UCF 101-24 benchmarks.  ...  Related work Action prediction and anticipation literature can be classified into deep learning and non-deep learning-based methods.  ... 
arXiv:1808.00141v1 fatcat:4rzxmxnoyfdl3lyinmm2u7ngge

Action Anticipation by Predicting Future Dynamic Images [chapter]

Cristian Rodriguez, Basura Fernando, Hongdong Li
2019 Lecture Notes in Computer Science  
In this paper, we present a method for human action anticipation by predicting the most plausible future human motion.  ...  Our method outperforms the currently best performing action-anticipation methods by 4% on JHMDB-21, 5.2% on UT-Interaction and 5.1% on UCF 101-24 benchmarks.  ...  Acknowledgments We thank NVIDIA Corporation for the donation of the GPUs used in this work. Action Anticipation By Predicting Future Dynamic Images  ... 
doi:10.1007/978-3-030-11015-4_10 fatcat:kupdi2jxbbe5jmry24fcg6co54

Prediction of operator intentions by action forecasting in collaborative assembly tasks

Manuel Alejandro Ruiz Garcia, Damiana Salvalai, Fiora Pirri, Erwin Rauch
2019 Zenodo  
Under such a perspective, robots are endowed with self and environment awareness and are able to smartly interact with both humans and other machines.  ...  Under the assumption that operator's intentions are mainly focused on completing the assigned task, such intentions can be predicted in terms of the sequence of actions required to complete it.  ...  This research is being funded by the EU H2020 Marie Skłodowska-Curie grant agreement No 734713 SME 4.0 Project and EU H2020 ICT-23-2014 agreement No 643950 SecondHands Project.  ... 
doi:10.5281/zenodo.4782206 fatcat:v4j5cbaxcjd2lco6u73ta3utme

VRUNet: Multitask learning model for intent prediction of vulnerable road users

2020 IS&T International Symposium on Electronic Imaging Science and Technology  
Fast track article for IS&T International Symposium on Electronic Imaging 2020: Autonomous Vehicles and Machines proceedings.  ...  More recently, with the availability of open-source datasets for action recognition and automated driving, many machine learning/deep learning models are being developed.  ...  II RELATED WORK Activity Recognition and prediction Human activity recognition and prediction from video data has been studied for some time now.  ... 
doi:10.2352/issn.2470-1173.2020.16.avm-109 fatcat:imnp5oexene47jyvbwhxhzyr4q

A Survey of Human Action Recognition and Posture Prediction

Nan Ma, Zhixuan Wu, Yiu-ming Cheung, Yuchen Guo, Yue Gao, Jiahong Li, Beijyan Jiang
2022 Tsinghua Science and Technology  
Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in videos.  ...  In the past decade, tremendous progress has been made in the field, especially after the emergence of deep learning technologies.  ...  Acknowledgment The authors wish to thank Dian'en Zhang and Wenjuan Li from Beijing Union University, Beijing, China. We really thank anonymous reviewers' constructive suggestions.  ... 
doi:10.26599/tst.2021.9010068 fatcat:lygnvsm3unddnngyd7s3wkchjy
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