Semi-automatic image segmentation and object tracking framework for investigative and surveillance-oriented applications

Patrick De Smet; Ignace Bruyland

doi:10.1117/12.474736

19 July 2002 Semi-automatic image segmentation and object tracking framework for investigative and surveillance-oriented applications

Patrick De Smet, Ignace Bruyland

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Proceedings Volume 4709, Investigative Image Processing II; (2002) https://doi.org/10.1117/12.474736
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

In this paper we discuss a combination of several image processing and computer vision components for the purpose of semi-automatically delineating and tracking moving objects. First, we introduce our motion based segmentation framework which uses an improved watershed technique to obtain an image pre-segmentation, and an improved block or segment matching technique to obtain an initial estimation of the motion field. The initial pre-segmentation and motion estimation results are then fed into an additional component which reduces the typical watershed oversegmentation until only a few coherently moving objects remain. Next, we discuss two tools that can be used to improve or correct the obtained segmentation results. Also, we investigate a simple, yet efficient object oriented approach for tracking moving segments; we discuss the concept of truncated segment matching, which combines characteristics of both traditional block matching and feature based motion estimation processes. Additionally, we use polynomial motion models to describe and predict the observed motion. The proposed segment matching approach is shown to allow controllable and relatively fast computation, which is illustrated with image segmentation and video tracking results. Finally, we briefly discuss the use of these techniques within the domain of investigative and surveillance oriented applications.

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Patrick De Smet and Ignace Bruyland "Semi-automatic image segmentation and object tracking framework for investigative and surveillance-oriented applications", Proc. SPIE 4709, Investigative Image Processing II, (19 July 2002); https://doi.org/10.1117/12.474736

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KEYWORDS

Image segmentation

Motion estimation

Motion models

Image processing

Surveillance

Cameras

Video

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