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.
CITATION STYLE
Alletto, S., Serra, G., & Cucchiara, R. (2015). Egocentric object tracking: An odometry-based solution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9280, pp. 687–696). Springer Verlag. https://doi.org/10.1007/978-3-319-23234-8_63
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