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Erschienen in:
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Knowledge Transfer for Scene-Specific Motion Prediction

verfasst von : Lamberto Ballan, Francesco Castaldo, Alexandre Alahi, Francesco Palmieri, Silvio Savarese

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is mostly driven by their rich prior knowledge about the visual world, both in terms of (i) the dynamics of moving agents, as well as (ii) the semantic of the scene. In this work we exploit the interplay between these two key elements to predict scene-specific motion patterns. First, we extract patch descriptors encoding the probability of moving to the adjacent patches, and the probability of being in that particular patch or changing behavior. Then, we introduce a Dynamic Bayesian Network which exploits this scene specific knowledge for trajectory prediction. Experimental results demonstrate that our method is able to accurately predict trajectories and transfer predictions to a novel scene characterized by similar elements.

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Vorheriges Kapitel SDF-2-SDF: Highly Accurate 3D Object Reconstruction
Nächstes Kapitel Weakly Supervised Localization Using Deep Feature Maps
Fußnoten
1
We built on the code provided in [44]. The parameters to be set are the number of k-NN images (9 in our experiments) and of superpixels used to classify a segment (we used 5). We used an intersection kernel and set the MRF pairwise term to 6.
 
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Metadaten
Titel
Knowledge Transfer for Scene-Specific Motion Prediction
verfasst von
Lamberto Ballan
Francesco Castaldo
Alexandre Alahi
Francesco Palmieri
Silvio Savarese
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46447-3

Electronic ISBN: 978-3-319-46448-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46448-0_42