Tracking objects moving around a person is one of the key steps in human visual augmentation: we could estimate their locations when they are out of our field of view, know their position, distance or velocity just to name a few possibilities. This is no easy task: in this paper, we show how current state-of-the-art visual tracking algorithms fail if challenged with a first-person sequence recorded from a wearable camera attached to a moving user. We propose an evaluation that highlights these algorithms' limitations and, accordingly, develop a novel approach based on visual odometry and 3D localization that overcomes many issues typical of egocentric vision. We implement our algorithm on a wearable board and evaluate its robustness, showing in our preliminary experiments an increase in tracking performance of nearly 20\% if compared to currently state-of-the-art techniques.

Egocentric Object Tracking: An Odometry-Based Solution / Alletto, Stefano; Serra, Giuseppe; Cucchiara, Rita. - 9280:(2015), pp. 687-696. (Intervento presentato al convegno International Conference on Image Analysis and Processing tenutosi a Genova nel 5-11 September 2015) [10.1007/978-3-319-23234-8_63].

Egocentric Object Tracking: An Odometry-Based Solution

ALLETTO, STEFANO;SERRA, GIUSEPPE;CUCCHIARA, Rita
2015

Abstract

Tracking objects moving around a person is one of the key steps in human visual augmentation: we could estimate their locations when they are out of our field of view, know their position, distance or velocity just to name a few possibilities. This is no easy task: in this paper, we show how current state-of-the-art visual tracking algorithms fail if challenged with a first-person sequence recorded from a wearable camera attached to a moving user. We propose an evaluation that highlights these algorithms' limitations and, accordingly, develop a novel approach based on visual odometry and 3D localization that overcomes many issues typical of egocentric vision. We implement our algorithm on a wearable board and evaluate its robustness, showing in our preliminary experiments an increase in tracking performance of nearly 20\% if compared to currently state-of-the-art techniques.
2015
International Conference on Image Analysis and Processing
Genova
5-11 September 2015
9280
687
696
Alletto, Stefano; Serra, Giuseppe; Cucchiara, Rita
Egocentric Object Tracking: An Odometry-Based Solution / Alletto, Stefano; Serra, Giuseppe; Cucchiara, Rita. - 9280:(2015), pp. 687-696. (Intervento presentato al convegno International Conference on Image Analysis and Processing tenutosi a Genova nel 5-11 September 2015) [10.1007/978-3-319-23234-8_63].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1084044
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