An Agile Framework for Real-Time Visual Tracking in Videos
Abstract
We present an agile framework for automated tracking of moving objects in full motion video (FMV). The framework is robust, being able to track multiple foreground objects of different types (e.g., person, vehicle) having disparate motion characteristics (like speed, uniformity) simultaneously in real time under changing lighting conditions, background, and disparate dynamics of the camera. It is able to start tracks automatically based on a confidence-based spatio-temporal filtering algorithm and is able to follow objects through occlusions. Unlike existing tracking algorithms, with high likelihood, it does not lose or switch tracks while following multiple similar closely-spaced objects. The framework is based on an ensemble of tracking algorithms that are switched automatically for optimal performance based on a performance measure without losing state. Only one of the algorithms, that has the best performance in a particular state is active at any time providing computational advantages over existing ensemble frameworks like boosting. A C++ implementation of the framework has outperformed existing visual tracking algorithms on most videos in the Video Image Retrieval and Analysis Tool (VIRAT: www.viratdata.org) and the Tracking-Learning-Detection data-sets.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 05, 2012
- Accession Number
- ADA581034
Entities
People
- Jerry Weltman
- Malcolm Stagg
- Manohar Karki
- Robert Dibiano
- Saikat Basu
- Supratik Mukhopadhyay
Organizations
- Louisiana State University