Pareto Optimization of Parameter Selection Speeds Up and Improves Quality of Motion Computation: Applying Evolutionary Multi-objective Optimization to Randomized-Subspace Robust PCA

Grob, David; Vurkaç, Mehmet; Miguel, Agnieszka; Mandich, Mirka; Bayrakcsmith, Rana

doi:10.1109/icdmw.2018.00134

Cited by 1 publication

(1 citation statement)

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“…This camouflage issue is of paramount importance in many applications in the field of conservation biology wherein cameras are placed in the wild to observe and analyze populations of endangered species. For example, [1][2][3] consider image sequences of highly elusive snow leopards. The snow leopard is a vulnerable species found throughout Central Asia, where poaching and ecological disruptions threaten its survival.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution DECOLOR for camouflaged moving foreground detection using a redundant wavelet transform

M.¹,

E.²,

Agnieszka³

2022

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Detection of moving foreground objects is essential to many image-sequence-analysis applications. However, preexisting methods tend to work best when the foreground is visually distinct from the background, suffering when objects are camouflaged. To address this shortcoming, a foreground-extraction algorithm resilient to camouflage is proposed by incorporating a redundant discrete wavelet transform into the well-known DECOLOR technique based on a sparse and low-rank model of the foregroundextraction problem. Detection of camouflaged moving objects is enhanced as a result of the combination of multiple background estimates in independent wavelet subbands into an overall estimate of the background, leveraging the known robustness of redundant wavelet transforms to additive noise. Experimental results demonstrate that the proposed method offers robustness to camouflage superior to that of other competing methods for image sequences containing snow leopards in the wild.

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Section: Introductionmentioning

confidence: 99%