Moving object tracking based on multi-independent features distribution fields with comprehensive spatial feature similarity

Li, Zhiyong; Yu, Xiaoping; Li, Pengfei; Hashem, Mervat

doi:10.1007/s00371-014-1044-0

Cited by 7 publications

(1 citation statement)

References 26 publications

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“…Furthermore, HOG features are introduced into the feature selection framework, which can improve the object representation ability [12, 13]. Objects are segmented to handle appearance changes of target objects, and descriptors or histograms are applied to each segment to make feature representation more robust to object's pose and partial occlusion in [14, 15].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2019

IET Computer Vision

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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 handcrafted 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.

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