K. Deepak

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 10

Top Topics

Anomaly Detection

Surveillance Videos

Top Venues

Signal Image Video Process.

Artif. Intell. Rev.

Circuits Syst. Signal Process.

J. Medical Imaging Health Informatics

Publications

Prabu Selvam, Muhammad Faheem, Vidyabharathi Dakshinamurthi, Akshaj Nevgi, R. Bhuvaneswari, K. Deepak, Joseph Abraham Sundar Koilraj
Batch Normalization Free Rigorous Feature Flow Neural Network for Grocery Product Recognition. IEEE Access 12 (2024)
S. Chandrakala, K. Deepak, G. Revathy
Anomaly detection in surveillance videos: a thematic taxonomy of deep models, review and performance analysis. Artif. Intell. Rev. 56 (4) (2023)
S. Chandrakala, K. Deepak, L. K. P. Vignesh
Bag-of-Event-Models based embeddings for detecting anomalies in surveillance videos. Expert Syst. Appl. 190 (2022)
S. Chandrakala, P. Shalmiya, V. Srinivas, K. Deepak
Object-centric and memory-guided network-based normality modeling for video anomaly detection. Signal Image Video Process. 16 (7) (2022)
K. Deepak, S. Chandrakala, C. Krishna Mohan
Residual spatiotemporal autoencoder for unsupervised video anomaly detection. Signal Image Video Process. 15 (1) (2021)
S. Chandrakala, V. Srinivas, K. Deepak
Residual Spatiotemporal Autoencoder with Skip Connected and Memory Guided Network for Detecting Video Anomalies. Neural Process. Lett. 53 (6) (2021)
K. Deepak, G. Srivathsan, S. Roshan, S. Chandrakala
Deep Multi-view Representation Learning for Video Anomaly Detection Using Spatiotemporal Autoencoders. Circuits Syst. Signal Process. 40 (3) (2021)
K. Deepak, L. K. P. Vignesh, S. Chandrakala
Autocorrelation of gradients based violence detection in surveillance videos. ICT Express 6 (3) (2020)
K. Deepak, Mohamed Yacin Sikkandar, S. Siddharth, S. Chandrakala
A Similarity Based Representation for Identifying Healthcare Anomalous Activities. J. Medical Imaging Health Informatics 10 (4) (2020)
R. Prashanth, K. Deepak, Amit Kumar Meher
High Accuracy Predictive Modelling for Customer Churn Prediction in Telecom Industry. MLDM (2017)