Robust Fragments-based Tracking using the Integral Histogram

We present a novel algorithm (which we call "FragTrack") for tracking an object in a video sequence. The template object is represented by multiple image fragments or patches. The patches are arbitrary and are not based on an object model (in contrast with traditional use of modelbased parts e.g. limbs and torso in human tracking). Every patch votes on the possible positions and scales of the object in the current frame, by comparing its histogram with the corresponding image patch histogram. We then minimize a robust statistic in order to combine the vote maps of the multiple patches. A key tool enabling the application of our algorithm to tracking is the integral histogram data structure [18]. Its use allows to extract histograms of multiple rectangular regions in the image in a very efficient manner. Our algorithm overcomes several difficulties which cannot be handled by traditional histogram-based algorithms [8, 6]. First, by robustly combining multiple patch votes, we a...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Amit Adam, Ehud Rivlin, Ilan Shimshoni
• Keywords: Computer Vision, CVPR 2006, Image Patch Histogram, Integral Histogram Data, Multiple Patch Votes, Robust Tracking, Tracking Large Targets