Deep Learning-Based Improved Object Recognition in Warehouses

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)


Research migrates in recent years from model-based object detection and classification to data-driven approaches. With the efficiency improvement of computational resources, improved acquisition systems, and bulks of data for training, deep learning models have found their way to accurate object category classification. Deep convolution nets have an inherent ability to extract features automatically and are used for accurate category classification. This paper has three parts. First, we extract moving foregrounds by using a mixture-of-Gaussians technique. Next, we aim at improving the quality of object foreground based on a pixel saliency map. Third, the obtained improved foreground is assigned labels using a pre-trained deep learning detector. Altogether, the paper proposes a way for improved video-based object detection and classification for logistics in warehouses.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringAuckland University of TechnologyAucklandNew Zealand
  2. 2.Crown Lift Trucks Ltd.AucklandNew Zealand

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