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2018 | OriginalPaper | Chapter

Instance-level Object Recognition Using Deep Temporal Coherence

Authors : Miguel Lagunes-Fortiz, Dima Damen, Walterio Mayol-Cuevas

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

In this paper we design and evaluate methods for exploiting temporal coherence present in video data for the task of instance object recognition. First, we evaluate the performance and generalisation capabilities of a Convolutional Neural Network for learning individual objects from multiple viewpoints coming from a video sequence. Then, we exploit the assumption that on video data the same object remains present over a number of consecutive frames. A-priori knowing such number of consecutive frames is a difficult task however, specially for mobile agents interacting with objects in front of them. Thus, we evaluate the use of temporal filters such as Cumulative Moving Average and a machine learning approach using Recurrent Neural Networks for this task. We also show that by exploiting temporal coherence, models trained with a few data points perform comparably to when the whole dataset is available.

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Title: Instance-level Object Recognition Using Deep Temporal Coherence
Authors: Miguel Lagunes-Fortiz
Dima Damen
Walterio Mayol-Cuevas
Publisher: Springer International Publishing
Book: Advances in Visual Computing
Print ISBN: 978-3-030-03800-7

Electronic ISBN: 978-3-030-03801-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-03801-4_25

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