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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Vydáno v:	IET computer vision Ročník 13; číslo 2; s. 157 - 164
Hlavní autoři:	Li, Yundong, Zhang, Xueyan, Li, Hongguang, Zhou, Qichen, Cao, Xianbin, Xiao, Zhifeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 01.03.2019 Wiley
Témata:	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
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Popis
Shrnutí:	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 complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)-based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy.
ISSN:	1751-9632 1751-9640 1751-9640
DOI:	10.1049/iet-cvi.2018.5129