Object Tracking and Anomaly Detection in Full Motion Video

Zakharov, Igor; Ma, Yue; Henschel, Michael D.; Bennett, John C.; Parsons, G.

doi:10.1109/igarss46834.2022.9884365

Cited by 3 publications

(1 citation statement)

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“…Salient Object Detection (SOD) plays a crucial role in the field of computer vision, with its primary objective being the identification and accentuation of the most visually engaging objects within a scene [ 1 , 2 ]. These objects typically draw the majority of observer attention and play a vital role in image and video processing tasks, such as object tracking [ 3 , 4 ], image segmentation [ 5 , 6 ], and scene understanding [ 7 , 8 ]. With the rapid advancement of depth sensor technology, RGB-D salient object detection has elicited significant interest among researchers.…”

Section: Introductionmentioning

confidence: 99%

SLMSF-Net: A Semantic Localization and Multi-Scale Fusion Network for RGB-D Salient Object Detection

Peng,

Zhai,

Feng

2024

Sensors

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Salient Object Detection (SOD) in RGB-D images plays a crucial role in the field of computer vision, with its central aim being to identify and segment the most visually striking objects within a scene. However, optimizing the fusion of multi-modal and multi-scale features to enhance detection performance remains a challenge. To address this issue, we propose a network model based on semantic localization and multi-scale fusion (SLMSF-Net), specifically designed for RGB-D SOD. Firstly, we designed a Deep Attention Module (DAM), which extracts valuable depth feature information from both channel and spatial perspectives and efficiently merges it with RGB features. Subsequently, a Semantic Localization Module (SLM) is introduced to enhance the top-level modality fusion features, enabling the precise localization of salient objects. Finally, a Multi-Scale Fusion Module (MSF) is employed to perform inverse decoding on the modality fusion features, thus restoring the detailed information of the objects and generating high-precision saliency maps. Our approach has been validated across six RGB-D salient object detection datasets. The experimental results indicate an improvement of 0.20~1.80%, 0.09~1.46%, 0.19~1.05%, and 0.0002~0.0062, respectively in maxF, maxE, S, and MAE metrics, compared to the best competing methods (AFNet, DCMF, and C2DFNet).

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