Video Analytics on IoT devices
Sree Premkumar, Vimal Premkumar, and Rakesh Dhakshinamurthy

TL;DR
This paper compares deep learning and traditional computer vision methods for video analytics on IoT devices, highlighting the most suitable approach based on performance and efficiency considerations.
Contribution
It provides a comparative analysis of DL-based and CV-based video analytics approaches specifically tailored for IoT devices.
Findings
DL approaches enable offline execution of large networks on IoT devices
Comparison shows strengths and weaknesses of DL and CV methods for IoT video analytics
Discussion identifies the most suitable approach for different IoT scenarios
Abstract
Deep Learning (DL) combined with advanced model optimization methods such as RC-NN and Edge2Train has enabled offline execution of large networks on the IoT devices. In this paper, we compare the modern Deep Learning (DL) based video analytics approaches with the standard Computer Vision (CV) based approaches and finally, discuss the best-suited approach for video analytics on IoT devices.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Surveillance and Tracking Methods · Human Pose and Action Recognition · Advanced Image and Video Retrieval Techniques
