Object tracking using mean shift and active contours

Authors:
Jae Sik Chang;Eun Yi Kim;KeeChul Jung;Hang Joon Kim
Affiliations:
Dept. of Computer Engineering, Kyungpook National Univ., South Korea;Scool of Internet and Multimedia, NITRI (Next-Generation Innovative Technology Research Institute), Konkuk Univ., South Korea;School of Media, College of Information Science, Soongsil University;Dept. of Computer Engineering, Kyungpook National Univ., South Korea
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

Citing 8
Cited 0

Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Sectored Snakes: Evaluating Learned-Energy Segmentations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Tracking via Level Set PDEs without Motion Computation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active contours without edges

IEEE Transactions on Image Processing
Active contours for tracking distributions

IEEE Transactions on Image Processing
Combining shape prior and statistical features for active contour segmentation

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.01

Visualization

Abstract

Active contours based tracking methods have widely used for object tracking due to their following advantages. 1) effectiveness to descript complex object boundary, and 2) ability to track the dynamic object boundary. However their tracking results are very sensitive to location of the initial curve. Initial curve far form the object induces more heavy computational cost, low accuracy of results, as well as missing the highly active object. Therefore, this paper presents an object tracking method using a mean shift algorithm and active contours. The proposed method consists of two steps: object localization and object extraction. In the first step, the object location is estimated using mean shift. And the second step, at the location, evolves the initial curve using an active contour model. To assess the effectiveness of the proposed method, it is applied to synthetic sequences and real image sequences which include moving objects.