---
title: "Robust Object Tracking with a Case-Base Updating Strategy"
authors:
  - "Wenhui Liao"
  - "Yan Tong"
  - "Zhiwei Zhu"
  - "Qiang Ji"
doi: ""
url: "https://ijcai.org/proceedings/2007/149"
pdf: "https://ijcai.org/proceedings/2007/0149.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "925-929"
track: ""
keywords:
---

# Robust Object Tracking with a Case-Base Updating Strategy

Wenhui Liao, Yan Tong, Zhiwei Zhu, Qiang Ji

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 925-929.

## Abstract

The paper describes a simple but effective framework for visual object tracking in video sequences. The main contribution of this work lies in the introduction of a case-based reasoning (CBR) method to maintain an accurate target model automatically and efficiently under significant appearance changes without drifting away. Specifically, an automatic case-base maintenance algorithm is proposed to dynamically update the case base, manage the case base to be competent and representative, and to maintain the case base in a reasonable size for real-time performance. Furthermore, the method can provide an accurate confidence measurement for each tracked object so that the tracking failures can be identified in time. Under the framework, a real-time face tracker is built to track human faces robustly under various face orientations, significant facial expressions, and illumination changes.