Object Recognition in Cluttered Environments

Ester Martinez-Martin and Angel P. del Pobil


Image Segmentation, Object Recognition


On the way to autonomous robots able to perceive all the surrounding space, this paper focuses on visual object recognition in real scenarios where those objects can be occluded, modify their appearance due to a viewpoint changed or different illumination conditions, etc. In this paper, we aim at robustly detecting and recognizing different objects in real-life scenarios from a visual input. For that, we present an appoach to properly build a peripersonal background model such that the target objects are always detected and recognized without any a-priori knowledge about the robotic system. Several objects and different scene conditions have been used to evaluate the method's performance by providing successful object detection and recognition in all the cases, as shown in the experimental section.

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