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A Spatial-Temporal based Next Frame Prediction and Unsupervised Classification of Video Anomalies in Real Time Estimation
2021
International Journal of Engineering and Advanced Technology
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term memories (ConvLSTM2D) as we are processing video frames and we predict the anomaly in real time using Euclidean distance between the generated and the ground truth frame and we achieved a real time ...
Hence, in this thesis we presented a novel approach for learning motion features and modeling normal Spatio-temporal dynamics for anomaly detection. ...
Instead of Memory Mapped mechanism a similar kind of architecture with auto encoders and U-Net architecture were proposed by Tong-Nguyen [11] [12] in his both research papers for anomaly detection ...
doi:10.35940/ijeat.a3161.1011121
fatcat:otbqp2qrinck7p5pfibz3r3edq
ViolenceNet: Dense Multi-Head Self-Attention with Bidirectional Convolutional LSTM for Detecting Violence
2021
Electronics
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memory (LSTM) module, that allows encoding relevant spatio-temporal features, to determine whether a video is violent or not. ...
the remaining one the accuracy drops in the worst case to 70.08% and in the best case to 81.51%, which points to future work oriented towards anomaly detection in new datasets. ...
The data presented in this study is openly available in https://github.com/FernandoJRS/violence-detection-deeplearning
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics10131601
fatcat:xac2hbengnbc3fwph2vjubl3yu
Multiple Instance Learning for Cheating Detection and Localization in Online Examinations
2024
IEEE Transactions on Cognitive and Developmental Systems
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2024 pre-print arXiv:2402.06107v1 fatcat:txjjxa7y4jccrg5k4dcc7cmpvu
These features are fed into the spatio-temporal graph module by stitching to analyze the spatio-temporal changes in video clips to detect the cheating behaviors. ...
Our experiments on three datasets, UCF-Crime, ShanghaiTech and Online Exam Proctoring (OEP), prove the effectiveness of our method as compared to the state-of-the-art approaches, and obtain the frame-level ...
They also used pseudo-labels to optimize the encoder, which shows that the spatio-temporal graph module can effectively improve prediction accuracy. ...
doi:10.1109/tcds.2024.3349705
fatcat:rjfc6vdbmzbnlevwd7ktwihn4q
DAM: Dissimilarity Attention Module for Weakly-supervised Video Anomaly Detection
2021
2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
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In order to decide instances to be normal or anomaly, DAM takes local spatio-temporal (i.e. clips within a video) dissimilarities into account rather than the global temporal context of a video. ...
The proposed framework along with DAM is validated on two large scale anomaly detection datasets i.e. ...
The authors are also grateful to the OPAL infrastructure from Université Côte d'Azur for providing resources and support. ...
doi:10.1109/avss52988.2021.9663810
fatcat:4gtgkduwrzailk7gtq5rvchfgu
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network
[article]
2022
arXiv
pre-print
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The RGB- stream generates a pseudo anomaly score and the flow stream generates a pseudo dynamicity score of a video segment. ...
The method has been evaluated on three popular video anomaly datasets, i.e., UCF-Crime, CCTV-Fights, and UBI-Fights. ...
Acknowledgement This work was supported in part by the Korea Institute of Science and Technology (KIST) Institutional Program under Project 2E31082 and in part by the National Research Foundation (NRF) ...
arXiv:2211.00882v1
fatcat:binxnntj35bfxmkk35nugcqy7e
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. ...
Mesgaran, M., +, TMM 2021 3931-3942 Anomaly detection Multi-Encoder Towards Effective Anomaly Detection in Videos. ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
Research and application of Transformer based anomaly detection model: A literature review
[article]
2024
arXiv
pre-print
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To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. ...
Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. ...
Acknowledgement The authors thank the anonymous reviewers for their insightful suggestions on this work. ...
arXiv:2402.08975v1
fatcat:hxwevh2hivakfjpqaibwlzobby
A Review of Deep Learning-based Human Activity Recognition on Benchmark Video Datasets
2022
Applied Artificial Intelligence
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Among all these, HAR is one of the challenging tasks and thrust areas of video data processing research. ...
This paper aims to present a comparative review of vision-based human activity recognition with the main focus on deep learning techniques on various benchmark video datasets comprehensively. ...
Further, the authors experimented with the Spatio-temporal VLAD (ST-VLAD) model, an extended encoding method that includes Spatio-temporal data at the encoding stage. ...
doi:10.1080/08839514.2022.2093705
fatcat:6on4g3sp3vaktnyyrk72k4mqta
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions
[article]
2022
arXiv
pre-print
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Our main findings are that the heterogeneities of pre-trained convolutional models have a negligible impact on crowd video anomaly detection performance. ...
Crowd anomaly detection is one of the most popular topics in computer vision in the context of smart cities. ...
[324] proposed a video anomaly detection algorithm based on future frame prediction using GAN and a self-attention mechanism [325] . ...
arXiv:2210.13927v1
fatcat:o766qvaj6jacbkskldrqz6gfqu
Gesture Recognition in Robotic Surgery: a Review
2021
IEEE Transactions on Biomedical Engineering
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Important future research directions include detection and forecast of gesture-specific errors and anomalies. ...
Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. ...
They also maintain memory mechanisms so that predictions depend in principle on all the previous time stamps (Fig. 5b) . ...
doi:10.1109/tbme.2021.3054828
pmid:33497324
fatcat:si5dcvrvnzc55dse6cst2k5tfi
Universal Time-Series Representation Learning: A Survey
[article]
2024
arXiv
pre-print
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This survey first presents a novel taxonomy based on three fundamental elements in designing state-of-the-art universal representation learning methods for time series. ...
According to the proposed taxonomy, we comprehensively review existing studies and discuss their intuitions and insights into how these methods enhance the quality of learned representations. ...
It is trained on a predictive attention mechanism over the set of compressed memories. ...
arXiv:2401.03717v1
fatcat:zy6pafj4vjac7fpj7umfxjju3y
A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis
[article]
2022
arXiv
pre-print
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Each paper is analyzed and commented on from both the methodology and application perspective. ...
We categorized the papers in (i) fetal standard-plane detection, (ii) anatomical-structure analysis, and (iii) biometry parameter estimation. ...
To process the temporal information encoded in the US videos, the framework makes use of a long short-term memory (LSTM). ...
arXiv:2201.12260v1
fatcat:hewsv3i3vfbzjjt3sakunb2g2a
Hierarchical Representations for Spatio-Temporal Visual Attention Modeling and Understanding
[article]
2023
arXiv
pre-print
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Thesis concerns the study and development of hierarchical representations for spatio-temporal visual attention modeling and understanding in video sequences. ...
Secondly, we develop a deep network architecture for visual attention modeling, which first estimates top-down spatio-temporal visual attention, and ultimately serves for modeling attention in the temporal ...
Koch and Ullman [73] Itti et al. [81] Sprage and Ballard [60] Torralba [82] Bruce and Tsotsos (AIM) [58] Navalpakkam and Itti [83] Itti and Baldi [59] Harel et al. ...
arXiv:2308.05189v1
fatcat:6li5ybdhgncgvfh4jprri7r7u4
Universal 3-Dimensional Perturbations for Black-Box Attacks on Video Recognition Systems
[article]
2021
arXiv
pre-print
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However, such attacks may overly perturb the videos without learning the spatio-temporal features (across temporal frames), which are commonly extracted by DNN models for video recognition. ...
We have conducted extensive experiments to evaluate U3D on multiple DNN models and three large-scale video datasets. The experimental results demonstrate its superiority and practicality. ...
CNS-1745894 and CNS-2046335. We would like to thank Zhaorui Liu and Junwen Chen for their help on some preliminary results and figures. ...
arXiv:2107.04284v2
fatcat:zaap3z7edjbntdajsaba6kebyu
Transformers in Vision: A Survey
[article]
2021
arXiv
pre-print
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processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation ...
memory (LSTM). ...
Manoj Kumar (Google Brain) for their helpful feedback on the survey. ...
arXiv:2101.01169v4
fatcat:ynsnfuuaize37jlvhsdki54cy4
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