Learning disentangled representation of video for pallet decomposition in industrial warehouses

Ikram Eddahmani; Chi-Hieu Pham; Thibault Napoléon; Isabelle Badoc; Marwa El-Bouz

doi:10.1117/12.2663161

13 June 2023 Learning disentangled representation of video for pallet decomposition in industrial warehouses

Ikram Eddahmani, Chi-Hieu Pham, Thibault Napoléon, Isabelle Badoc, Marwa El-Bouz

Proceedings Volume 12527, Pattern Recognition and Tracking XXXIV; 1252704 (2023) https://doi.org/10.1117/12.2663161
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Over the past decade, several approaches have been proposed to learn disentangled representations for video prediction. However, reported experiments are mostly based on standard benchmark datasets such as Moving MNIST and Bouncing Balls. In this work, we address the problem of learning disentangled representation for video prediction in an industrial environment. To this end, we use decompositional disentangled variational auto-encoder, a deep generative model that aims to decompose and recognize overlapped boxes on a pallet. Specifically, this approach disentangles each frame into a dynamic component (box appearance) and a temporally variant component (box location). We evaluate this approach on a new dataset, which contains 40000 video sequences. The experimental results demonstrate the ability to learn both the decomposition of the bounding boxes and their reconstruction without explicit supervision.

Conference Presentation

Citation Download Citation

Ikram Eddahmani, Chi-Hieu Pham, Thibault Napoléon, Isabelle Badoc, and Marwa El-Bouz "Learning disentangled representation of video for pallet decomposition in industrial warehouses", Proc. SPIE 12527, Pattern Recognition and Tracking XXXIV, 1252704 (13 June 2023); https://doi.org/10.1117/12.2663161

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