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Dr. Deepak K.

Assistant Professor, Department of Computer Science and Engineering , School of Computing, Chennai

Qualification: B. Tech., M.Tech., Ph.D
k_deepak@ch.amrita.edu
Google Scholar Profile
Research Interest: Machine Learning, Deep Learning, Computer Visio, Video anomaly detection, Human activity detection, Vision based heartrate estimation

Bio

Dr. Deepak K. currently serves as an Assistant Professor, in the Department of Computer Science and Engineeirng, School of omputing, Chennai.

Current Research Project

  • Abnormal Human Activity monitoring using Computer vision and deep learning techniques

Awards

  • Awarded Senior Research Fellowship from Council of Scientific and Industrial Research
    (CSIR), India.

Post graduate and Undergraduate Projects

  • Industry Project:    Sales    forecasting   and    customer   behaviour      (Kibin Technologies, Chennai)
  • PG: Monitoring suspicious events in restricted environments with computer vision and machine learning (Extended as my PhD. research problem)
  • Mini project: Plant species identification in android operating system
Publications

Journal Article

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

Conference Paper

Year : 2021

Recent Trends and Study on Perspective Crowd Counting in Smart Environments

Cite this Research Publication : Jaswanth, V., Yeduguru, A.R., Manoj, V.S., Deepak, K. and Chandrakala, S., 2021. Recent Trends and Study on Perspective Crowd Counting in Smart Environments. In Artificial Intelligence and Technologies: Select Proceedings of ICRTAC-AIT 2020 (pp. 63-72). Singapore: Springer Singapore.(SCOPUS)

Publisher : Springer

Education
  • March 2022 to Present : Post-Doctorate
    Remote Heart Rate Estimation from Near-Infrared Videos using Computer vision and Deep learning techniques under the guidance of Yannick Benezeth, Maître de Conférences / Ass. Prof. Univ. Bourgogne Franche-Comté, ImViA EA 7535. 
  • September 2017- October 2021 : Ph. D.,
    Thesis titled Video anomaly detection using model driven embeddings for visual events under the guidance of Prof. Dr.S. Chandrakala Phd (IITM)
    School of Computing SASTRA Deemed University, Thanjavur, India
  • May 2015 : M.Tech
    Computer Science and Engineering, Rajalakshmi Engineering College ( Affiliated to Anna University, Chennai), Chennai, India.  (CGPA: 7.56)
  • May 2013 : B.Tech
    Information Technology, Panimalar Institute of Technology (Affiliated to Anna University, Chennai), Chennai, India,  (CGPA:6.83)
  • April 2007 : Higher Secondary
    DRBCCC School, Thiruvallur, April 2009 (71.58%) High School, G.R.T.M.Vivekananda Vidyalaya, Chennai  (72.8%)
Proficiency in Programming Languages
  • Matlab
  • Python
  • Pytorch
  • Keras
  • C, C++
  • R studio
Workshops Attended
  1. Video Analytics work shop conducted at Anna University MIT Campus Chennai (2014)
  2. Work shop on Ethical Hacking, Multi touch, Android and Cloud Computing at Rajalakshmi Engineering college, Chennai (2013)
  3. Data science conclave organized by ARM analyzed held at Chennai (2015)
  4. Attended work shop on Frontiers in Computer
Laboratories Handled
  • C, C++
  • Data mining and Data warehousing
  • Data science
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