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What is RGBD Segmentation for Occlusion Tracking about?

This study presents an improved Mask R-CNN model that incorporates RGBD images for enhanced occlusion handling in multi-object tracking. By utilizing depth images alongside RGB data, the model demonstrates a 5% increase in mean Average Precision (mAP) and significantly reduces ID switching during tracking. The proposed method effectively maintains object tracking even during occlusions, showcasing the advantages of depth information in computer vision applications.

Author

noname

Language

EN