Mean shift-based object tracking in FLIR imagery using multiple features

Wei Yang; Junshan Li; Deqin Shi; Wen Cheng

doi:10.1117/12.832386

30 October 2009 Mean shift-based object tracking in FLIR imagery using multiple features

Wei Yang, Junshan Li, Deqin Shi, Wen Cheng

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74960T (2009) https://doi.org/10.1117/12.832386
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

A novel object tracking algorithm for FLIR imagery based on mean shift using multiple features is proposed to improve the tracking performance. First, the appearance model of infrared object is represented in the combination of gray space, LBP texture space, and orientation space with different feature weight. And then, the mean shift algorithm is employed to find the object location. An on-line feature weight update mechanism is developed based on Fisher criteria, which measure the discrimination of object and background effectively. Experiment results demonstrate the effectiveness and robustness of the proposed method for object tracking in FLIR imagery.

Citation Download Citation

Wei Yang, Junshan Li, Deqin Shi, and Wen Cheng "Mean shift-based object tracking in FLIR imagery using multiple features", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960T (30 October 2009); https://doi.org/10.1117/12.832386

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