Object detection and tracking under Complex environment using deep learning-based LPM

Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand-crafted features, which are not sufficiently robust to a co...

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Veröffentlicht in:	IET computer vision Jg. 13; H. 2; S. 157 - 164
Hauptverfasser:	Li, Yundong, Zhang, Xueyan, Li, Hongguang, Zhou, Qichen, Cao, Xianbin, Xiao, Zhifeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 01.03.2019 Wiley
Schlagworte:	colour histograms deep learning-based LPM feature extraction hand-crafted features histograms of oriented gradients image colour analysis learning (artificial intelligence) local probability model local probability model-based mean shift algorithm LPM-based mean shift algorithm MS tracking algorithm neural nets object detection object tracking probability Special Issue: Visual Domain Adaptation and Generalisation stacked denoising autoencoders stacked denoising autoencoders local probability model-based mean shift algorithm probability deep learning-based LPM colour histograms object detection MS tracking algorithm LPM-based mean shift algorithm local probability model feature extraction histograms of oriented gradients hand-crafted features object tracking image colour analysis learning (artificial intelligence) neural nets
ISSN:	1751-9632, 1751-9640, 1751-9640
Online-Zugang:	Volltext
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