Multi-feature target tracking based on lifting scheme

Yang, Xinli; Huixian, Yang; Zeng, Jinfang; Yu, Hong

doi:10.1109/icemi.2009.5274033

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Mean-shift tracking, proposed by Comaniciu et al [16,17], is an efficient algorithm for non-rigid object tracking. It uses mean-shift iterations to find the most probable target location, and both the target and the target candidate are modelled as two probability distributions.…”

Section: Mean-shift Tracking Algorithmmentioning

confidence: 99%

A multi-cue mean-shift target tracking approach based on fuzzified region dynamic image fusion

Xiao

Wu³

2012

Sci. China Inf. Sci.

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Traditional target tracking algorithms based on single sensor images are unstable and have low accuracy. Based on regional target detection and fuzzy region rules, a fuzzy region-based multi-sensor image fusion approach is proposed in this paper. The similarity measure weight is adapted to this dynamic image fusion algorithm, while the tracking method uses the proposed multi-cue mean-shift tracking algorithm. Three experimental results using real world image sequences are evaluated using the steady state square root mean error. The fusion and tracking experiments indicate that the proposed approach is effective and efficient when aiming at a target moving from one area to a different area, which meets the robustness and real-time requirements. CitationXiao G, Yun X, Wu J M. A multi-cue mean-shift target tracking approach based on fuzzified region dynamic image fusion.

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Section: Mean-shift Tracking Algorithmmentioning

confidence: 99%

A multi-cue mean-shift target tracking approach based on fuzzified region dynamic image fusion

Xiao

Wu³

2012

Sci. China Inf. Sci.

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“…(2) By use of the basic concept of the wavelet transform, an image can be transformed by dilation translation. And, video image is seen as two-dimension signal, divided by serial number of row and column into four sub parts: row and column odd-numbered sequence, row odd-numbered column even-numbered sequence, row even-numbered column odd-numbered sequence and row and column even-numbered sequence [3]. Based on this method, the original image is decomposed by two-dimensional discrete wavelet transformation.…”

Section: The Transform Of Discrete Orthogonal Waveletmentioning

confidence: 99%

Target Tracking Based on Particle Filter with Multi-Path Particles

Sun

Chao

2011

AMM

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Visual tracking is a key issue for autonomous navigation, intelligent monitoring system and so on. While a Particle Filter for tracking is designed, the target in image may be out of range, and the tracked target will be lost since the target image size keeps changing. A method for tracking a mobile target with visual image, Particle Filter with multi-path particles (PFWMP), is proposed to solve the above problem in this paper. Here, the method is based on the wavelet transform incorporated in traditional Particle Filter, and particles are made to move in original image and the image processed by wavelet transform. The results show that the improved approach using multiple particles can enhance the robustness of tracking algorithm and improve tracking accuracy.

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