Object tracking in video signals using Compressive Sensing
This work addresses video compression for object tracking, but it appears incremental as it applies existing methods to new data without major breakthroughs.
The paper tackled the problem of reducing pixel count in video signals for object tracking using Compressive Sensing, achieving satisfactory results with minimal changes in trajectory graphs even when reducing pixels from 45% down to 1%.
Reducing the number of pixels in video signals while maintaining quality needed for recovering the trace of an object using Compressive Sensing is main subject of this work. Quality of frames, from video that contains moving object, are gradually reduced by keeping different number of pixels in each iteration, going from 45% all the way to 1%. Using algorithm for tracing object, results were satisfactory and showed mere changes in trajectory graphs, obtained from original and reconstructed videos.