language: en

Moving target classification and tracking from real-time video

Alan J. Lipton Hironobu Fujiyoshi Raju S. Patil
The Robotics Institute. Carnegie Mellon University
5000 Forbes Avenue, Pittsburgh, PA, 15213
email: fajljhironobujrajug@cs.cmu.edu

Abstract This paper describes an end-to-end method for extracting moving targets from a real-time video stream, classifying them into predefined categories according to image-based properties, and then robustly tracking them. Moving targets are detected using the pixelwise difference between consecutive image frames. A classi-ficatoin metric is applied these targets with a temporal consistency constraint to classify them into three categories: human, vehicle or background clutter. Once classified, targets are tracked by a combination of temporal differencing and template matching. The resulting system robustly identifies targets of interest, rejects background clutter, and continually tracks over large distances and periods of time despite occlusions, appearance changes and cessation of target motion.

1 Introduction