Developing countries face a growing demand for video analytics, yet often lack sufficient computational resources. This paper addresses this challenge by proposing and evaluating optimization techniques for efficient video stream processing on resource-constrained devices, including edge systems. We introduce and evaluate several techniques, including image resizing, frame skipping, parallel processing, threading, queue management, memory optimization, and buffering. Experimental results demonstrate substantial improvements in frames per second (FPS) and memory usage, enabling real-time video analytics without compromising accuracy. By effectively balancing performance and resource consumption, our methods facilitate the deployment of advanced AI-driven video analysis in resource-limited environments, paving the way for practical real-time monitoring and alert systems.

RSTP stream, Real-time Video Analytics, Computational Optimization, Post-Training Optimization