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Erschienen in:
Design of a Simulation System for Cross-Eye Dynamic Jamming Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Vehicle Tracking Based on Structured Output SVM Using Retinex and Mutual Information

verfasst von : Longqi Wang, Xinyang Li, Guanyu Lin, Wei Pei, Guanrong Wang

Erschienen in: Proceedings of 2017 Chinese Intelligent Automation Conference

Verlag: Springer Singapore

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Abstract

At present, most vehicle tracking algorithms are tested in an ideal environment (definition video, without attitude change, without occlusion). However, due to the vehicle variability (scale change, attitude change) and complexity (noise, occlusion) of the surrounding environment in real-life scenarios, there are still many difficulties for the vehicle target accurately tracking. In order to adapt to blur, occlusion, fast moving, illumination change and other complex situation in real scene, the multi-scale Retinex image enhancement algorithm and the mutual information occlusion processing algorithm are introduced into structured output SVM tracking algorithm. Experiments show that the tracking accuracy and robustness of the method are improved significantly on the premise of tracking task performance in the above case.

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Vorheriges Kapitel Research of Localization Method Based on Virtual Reference Points in Robot Auditory System
Nächstes Kapitel Neural Network-Based Self-triggered Attitude Control of a Rigid Spacecraft
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Metadaten
Titel
Vehicle Tracking Based on Structured Output SVM Using Retinex and Mutual Information
verfasst von
Longqi Wang
Xinyang Li
Guanyu Lin
Wei Pei
Guanrong Wang
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Proceedings of 2017 Chinese Intelligent Automation Conference

Print ISBN: 978-981-10-6444-9

Electronic ISBN: 978-981-10-6445-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-6445-6_76

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