arXiv:1903.06253
Object tracking in video signals using Compressive Sensing
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking
ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking
Asynchronous Multi-Object Tracking with an Event Camera
A Simple Detector with Frame Dynamics is a Strong Tracker
LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics
토론
이 논문과 연결된 포럼 글이 아직 없습니다.