Object Detection and Localization Using Compressed Sensing

Deotale, Poonam Ashok; Vinayakray-Jani, Preetida

doi:10.1007/978-3-319-67934-1_12

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Lack Of Judgment On Occlusion1

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2018

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“…When the target is occluded, the extracted compressed feature of the target will undoubtedly lead the samples to be incorrectly classified. In other words, if the classifier is updated on the basis of wrong positive and negative sample, it will result in a decline in the classification performance of the classifier [15]. Therefore, the updating process of the original algorithm is blind and lacks the basic decision mechanism.…”

Section: Lack Of Judgment On Occlusionmentioning

confidence: 99%

Improved CT algorithm based on target block division and feature points matching

Wang

Jiang

Yan

et al. 2018

J Image Video Proc.

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For compressive tracking (CT) algorithm, it is vulnerable to the occlusion, when tracking targets. An improved CT algorithm based on target division and feature point matching is proposed in this paper, which can determine different target tracking states by the method of target division. When the target is in normal tracking or partial occlusion, the target is located accurately by the sub-block with the highest discrimination degree. In this scenario, the classifier only updates the unblocked sub regions in order to avoid the error of updating the occlusion information. When the target is completely occluded or lost in some frames, ORB feature matching is used to relocate the target. Experimental results show that our proposed CT algorithm can improve the robustness of the algorithm and reduces the drift problem.

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Section: Lack Of Judgment On Occlusionmentioning

confidence: 99%