Abstract
Organising and preprocessing are crucial steps in order to perform analysis on lifelogs. This paper presents a method for preprocessing, enriching, and segmenting lifelogs based on GPS trajectories and images captured from wearable cameras. The proposed method consists of four components: data cleaning, stop/trip point classification, post-processing, and event characterisation. The novelty of this paper lies in the incorporation of a visual module (using a pretrained CLIP model) to improve outlier detection, correct classification errors, and identify each event’s movement mode or location name. This visual component is capable of addressing imprecise boundaries in GPS trajectories and the partition of clusters due to data drift. The results are encouraging, which further emphasises the importance of visual analytics for organising lifelog data.
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Tran, LD., Nie, D., Zhou, L., Nguyen, B., Gurrin, C. (2023). VAISL: Visual-Aware Identification of Semantic Locations in Lifelog. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_54
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