Year : 2024
Video-based heart rate estimation from challenging scenarios using synthetic video generation
Cite this Research Publication : Yannick Benezeth, Deepak Krishnamoorthy, Deivid Johan Botina Monsalve, Keisuke Nakamura, Randy Gomez, Johel Mitéran, Video-based heart rate estimation from challenging scenarios using synthetic video generation, Biomedical Signal Processing and Control, Elsevier BV, 2024, https://doi.org/10.1016/j.bspc.2024.106598
Publisher : Elsevier BV
Year : 2022
Anomaly detectionin surveillance videos: a thematic taxonomy of deep models, review and performance analysis.
Cite this Research Publication : Chandrakala,S.,Deepak,K.and Revathy,G.,2022.Anomaly detectionin surveillance videos: a thematic taxonomy of deep models, review and performance analysis. Artificial Intelligence Review,pp.1-50.(IF-9.55)
Publisher : Springer
Year : 2022
Object-centric and memory-guided network-based normality modeling for video anomaly detection
Cite this Research Publication : Chandrakala,S.,Shalmiya,P.,Srinivas,V.andDeepak,K.,2022.Object-centric and memory-guided network-based normality modeling for video anomaly detection. Signal,Image and Video Processing,pp.1-7.
Publisher : Springer
Year : 2022
Bag-of-Event-Models based embeddings for detecting anomalies in surveillance videos
Cite this Research Publication : Chandrakala, S., Deepak, K. and Vignesh, L.K.P., 2022. Bag-of-Event-Models based embeddings for detecting anomalies in surveillance videos . Expert Systems with Applications,190,p.116168.(IF6.954)
Publisher : Elsevier
Year : 2021
Residual spatiotemporal autoencoder with skip connected and memory guided network for detecting video anomalies
Cite this Research Publication : Chandrakala, S., Srinivas, V. and Deepak, K., 2021. Residual spatiotemporal autoencoder with skip connected and memory guided network for detecting video anomalies. Neural Processing Letters, 53(6), pp.4677-4692.
Publisher : Neural Processing Letters
Year : 2021
Deep Multi-View Representation Learning for Video Anomaly Detection using Spatio-Temporal Autoencoders
Cite this Research Publication : DeepakK,SrivatsanG,RoshanS,ChandrakalaS,“Deep Multi-View Representation Learning for Video Anomaly Detection using Spatio-Temporal Autoencoders”,published online, Circuits, Systems and Signal processing, Springer 2020(IF–2.22),doi:https://doi.org/10.1007/s00034-020-01522-7.
Publisher : Springer
Year : 2020
A Similarity Based Representation for Identifying Healthcare Anomalous Activities
Cite this Research Publication : Deepak, K., Sikkandar, M.Y., Siddharth, S. and Chandrakala, S., 2020. A Similarity Based Representation for Identifying Healthcare Anomalous Activities. Journal of Medical Imaging and Health Informatics, 10(4), pp.787-794
Publisher : Ingenta Connect
Year : 2020
Violence detection in automated video surveillance: Recent trends and comparative studies
Cite this Research Publication : Roshan, S., Srivathsan, G., Deepak, K. and Chandrakala, S., 2020. Violence detection in automated video surveillance: Recent trends and comparative studies. The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems, pp.157-171. (SCOPUS)
Publisher : Science Direct
Year : 2020
Autocorrelation of gradients based violence detection in surveillance videos
Cite this Research Publication : Deepak, K., Vignesh, L.K.P. and Chandrakala, S.J.I.E., 2020. Autocorrelation of gradients based violence detection in surveillance videos. ICT Express, 6(3), pp.155-159
Publisher : Science Direct