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Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models
[article]
2020
arXiv
pre-print
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A small subset of explainability techniques developed initially for image recognition models has recently been applied for interpretability of 3D Convolutional Neural Network models in activity recognition ...
In fact, it has been shown that these models do not appropriately factor motion information into their decision. ...
Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. ...
arXiv:2003.14285v1
fatcat:24b2igwemnfche3pjsrq2rat3e
Explainable Deep Learning for Video Recognition Tasks: A Framework Recommendations
[article]
2019
arXiv
pre-print
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The current work in explainability for video models is still overshadowed by Image techniques, while Video Deep Learning itself is quickly gaining on methods for still images. ...
This paper seeks to highlight the need for explainability methods designed with video deep learning models, and by association spatio-temporal input in mind, by first illustrating the cutting edge for ...
Framework for Future Research In this paper we have addressed some gaps in the current works for explainable activity recognition with video deep learning models. ...
arXiv:1909.05667v1
fatcat:jnjri66khfgedn4hyxtqaw55g4
Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition
[article]
2018
arXiv
pre-print
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In this paper we propose an end-to-end trainable deep neural network model for egocentric activity recognition. ...
We visually analyze attention maps generated by the network, revealing that the network successfully identifies the relevant objects present in the video frames which may explain the strong recognition ...
Several deep learning based methods have been proposed for action recognition from third person videos in recent years. ...
arXiv:1807.11794v1
fatcat:sqentueowrba5oesvn2wgxxsky
Video Activity Recognition: State-of-the-Art
2019
Sensors
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The aim of this paper is to survey the state-of-the-art techniques for video activity recognition while at the same time mentioning other techniques used for the same task that the research community has ...
Video activity recognition, although being an emerging task, has been the subject of important research efforts due to the importance of its everyday applications. ...
Conclusions In this paper, different methods for video activity recognition have been presented. Several models have been explained showing the development of recent years. ...
doi:10.3390/s19143160
fatcat:ore5paemmbfxzjimsvnnezgwwi
A Review on state-of-the-art Violence Detection Techniques
2019
IEEE Access
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The relevant literature presents different techniques for detection of such activities from the video proposed in the recent years. ...
Moreover, datasets and video features that used in the techniques, which play a vital role in recognition process are also discussed. ...
Inspired by the performance of deep models for the recognition of human action, an innovative method for the detection of human violent behavior by combining the trajectory and deep CNN is proposed [44 ...
doi:10.1109/access.2019.2932114
fatcat:v5bkozfisneprkvikxpi4scnkq
Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions
[article]
2017
arXiv
pre-print
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In this paper, we present a novel deep learning based approach for addressing the problem of interaction recognition from a first person perspective. ...
by more than 20% in recognition accuracy. ...
Acknowledgments We gratefully acknowledge the support of NVIDIA Corporation with the donation of GPU used for this research. ...
arXiv:1709.06495v1
fatcat:ikslmyzh2jednpwprc3ra6x5wi
Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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In this paper, we present a novel deep learning based approach for addressing the problem of interaction recognition from a first person perspective. ...
by more than 20% in recognition accuracy. ...
Acknowledgments We gratefully acknowledge the support of NVIDIA Corporation with the donation of GPU used for this research. ...
doi:10.1109/iccvw.2017.276
dblp:conf/iccvw/SudhakaranL17
fatcat:dmkd6noojjfvvekouzehqtuecy
Analysis of Deep Neural Networks For Human Activity Recognition in Videos – A Systematic Literature Review
2021
IEEE Access
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-What deep learning techniques currently exist since 2015 to model Spatio-temporal information to support activity recognition in videos? ...
[28] reviewed the activity recognition trends with deep learning models. This paper examined various models based on two-stream networks, C3D and RNN, used for activity recognition. ...
She has previously served as a lecturer in the field of Computer Science and IT and has one journal publication before. Her research interests are Data Science, Machine learning, and Computer vision. ...
doi:10.1109/access.2021.3110610
fatcat:ussooxm7azfljpb5prsm7creaa
LSTA: Long Short-Term Attention for Egocentric Action Recognition
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. ...
In this paper we propose LSTA as a mechanism to focus on features from relevant spatial parts while attention is being tracked smoothly across the video sequence. ...
We gratefully acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research. ...
doi:10.1109/cvpr.2019.01019
dblp:conf/cvpr/SudhakaranEL19
fatcat:numtqwnpdjgijhao3s4e7optii
A Comprehensive Review of Group Activity Recognition in Videos
2021
International Journal of Automation and Computing
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Second, we survey the group activity recognition methods, including those based on handcrafted features and those based on deep learning networks. ...
In this paper, we give a comprehensive overview of the advances in group activity recognition in videos during the past 20 years. ...
This section reviews deep learning based methods for group activity recognition. ...
doi:10.1007/s11633-020-1258-8
fatcat:ycka4thcy5a6vghpenpthtrndi
Explainable Video Action Reasoning via Prior Knowledge and State Transitions
[article]
2019
arXiv
pre-print
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Our method takes advantage of both classical reasoning and modern deep learning approaches. ...
Specifically, prior knowledge is defined as the information of a target video domain, including a set of objects, attributes and relationships in the target video domain, as well as relevant actions defined ...
Recently, with the success of deep learning in a variety of computer vision tasks, great progress has been achieved in video action recognition with various deep neural networks [3, 9, 41, 44, 48] . ...
arXiv:1908.10700v1
fatcat:itbcavk37fgkfmnhn5syzze6iy
Video Representation via Fusion of Static and Motion Features Applied to Human Activity Recognition
2019
KSII Transactions on Internet and Information Systems
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In human activity recognition system both static and motion information play crucial role for efficient and competitive results. ...
Second, trajectories are extracted by tracking key points and only those trajectories have been selected which are located in central region of the original video frame in order to to reduce irrelevant ...
deep learning-based models for action recognition. ...
doi:10.3837/tiis.2019.07.015
fatcat:evwphdggdnbivek3axqxmwumn4
A Novel Recurrent Neural Networks Architecture for Behavior Analysis
2021
˜The œinternational Arab journal of information technology
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In this paper, we propose new model for human behavior analysis which combine transfer learning model and Recurrent Neural Network (RNN). ...
In fact, in the increase of crimes, everyone needs video surveillance to keep their belongings safe and to automatically detect events by collecting important information for the assistance of security ...
Our proposed model for human action recognition relies on the extraction of relevant features from Inception v3. ...
doi:10.34028/iajit/18/2/1
fatcat:tm4mche26zezbljgr4dwcqtr3i
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
2019
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1905.04757v1 fatcat:sfvle3lkz5gw5b3c4as3q6n6zeOther Versions
We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. ...
In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames ...
Recently, deep learning based-approaches have been proposed for 3D human activity analysis [9] , [59] , [60] . ...
doi:10.1109/tpami.2019.2916873
pmid:31095476
fatcat:xlwrbwwucvdjpofkt5lwdyqy4m
Automatic Unusual Activities Recognition Using Deep Learning in Academia
2022
Computers Materials & Continua
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The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional ...
To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities ...
The proposed system extracts key-frames based on human motion from a video sequence/stream; deep learning model 2D and 3D CNN used for classification task to detect suspicious activities. ...
doi:10.32604/cmc.2022.017522
fatcat:cqtmvtdj6fcr7lm33fzomjhswm
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