Kernel-Based Online Object Tracking via Gaussian Mixture Model Learning

Miao, Qiguang; Gu, Yanfeng

doi:10.1109/imccc.2016.130

Cited by 3 publications

(1 citation statement)

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“…A very important core of opto‐electric target tracking is the representation modeling, which includes feature extraction and model learning, that is, to extract local electronic image features from target areas and use training and description to represent the area . As a result, it can be defined locally to the pixel packet .…”

Section: Introductionmentioning

confidence: 99%

Opto‐electric target tracking algorithm based on local feature selection and particle filter optimization

Peng

Huang

2018

Concurrency and Computation

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Summary Aiming at exploring the opto‐electric target tracking, which is an important technology in the field of computer vision, the binocular stereo vision camera opto‐electric target tracking is studied and and a multi feature fusion characterization modeling method locally weighted is proposed. The target area is divided into multiple sub‐image areas by the modeling method, the feature histogram after the background weighting is extracted, and the sub‐image region is taken as a basic unit for adjusting the feature weight. The sub‐image area selected is regarded as the significant area, and the significant area is further extracted and fused in particle filter tracking algorithm. Then, the obtained significant are is conducted with color distribution processing. In the state prediction stage, the Mean Shift algorithm is applied to optimize each particle so that it converges to the optimal position. The experiment results showed that the multi feature fusion representation modeling method has better tracking accuracy and stability compared with the traditional fusion method and after the color distribution treatment; it has strong anti‐jamming for background effect. It is concluded that using Mean Shift algorithm for particle optimization can further strengthen the accurate tracking of the targets.

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Section: Introductionmentioning

confidence: 99%