1School of Sciences and Humanities, SR University, Warangal, 506371, India
2Department of Electronics and Communication Engineering, Siddhartha Institute of Engineering and Technology, Ibrahimpatnam, Hyderabad, 501 506, Telangana, India
3Professor and Director, St Martin's Engineering College, Secunderabad, India
Email: drkasaravindra@gmail.com
Corresponding author: Sarath Chandra Veerla - sarathchandra.veerla85@gmail.com
ORCID: https://orcid.org/0000-0001-9288-9107
V Bhagya Raju: vbhagya01@gmail.com
ORCID: https://orcid.org/0000-0001-6781-0639
Human Action Recognition (HAR) from video streams has many possible uses in areas like healthcare, surveillance, and human-computer interaction. The original purpose of video compression methods like SPIHT and others was to work with pixel-level quality measurements like PSNR and SSIM. These indicators have nothing to do with how well recognition works. In this paper, we present a Task-Aware Progressive SPIHT Framework that prioritises spatio-temporal data critical to actions during compression. By combining efficient pose estimation algorithms with lightweight motion and posture cues from optical-flow magnitude maps, you can make a significance mask that shows the areas that are most important for understanding action. We present a 3D Temporal-Priority SPIHT method that utilises motion-based dependencies among video frames, alongside spatial and temporal dependencies. Additionally, a Policy-Gradient-based Bit-Dropping method and Weighted Significance Testing are used to dynamically give bits to coefficients that are more important for the skeleton and motion while hiding background information that isn't important. Experimental tests show that the proposed framework works well for video analytics applications that need to work in real time and have limited resources. It greatly improves action detection accuracy at low bitrates while keeping compression efficiency competitive.
Keywords: Compression that understands the job at hand; Progressive SPIHT, recognising human actions, encoding that knows about motion, optical flow, pose-guided compression, and 3D temporal coding are all examples. Improving the way bits are allocated; Video analytics that use fewer resources.
How to cite this article: Raju VB, Veerla SC, Ravindra K. Task-Aware Progressive SPIHT Framework for Efficient Action Recognition in Video Streams. Int J Drug Deliv Technol. 2026;16(7s): 681-685; DOI: 10.25258/ijddt.16.7s.72
Source of support: None
Conflict of interest: None