MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?

Fabbri, M.; Maugeri, Gianluca; Cetintas, Orcun; Gasparini, Riccardo; Ošep, Aljoša; Calderara, Simone; Leal-Taixé, Laura; Cucchiara, Rita

doi:10.48550/arxiv.2108.09518

Cited by 1 publication

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“…This will first accomplished by resuming and extending the data acquisition campaign in real conditions that were interrupted by the COVID-19 pandemic. Alternative approaches based on acquiring synthetic data using simulators will also be tested, as they provide a relatively cost-effective way to acquire additional data that could be used to boost the generalization capacity of our trained classifiers [55][56][57]. Finally, additional unobtrusive sensor-monitoring physiological modalities such as the HR or RR [47] will be considered in future studies.…”

Section: Discussionmentioning

confidence: 99%

Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car

Doniec

Piaseczna

et al. 2022

Electronics

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Driving a car is an activity that became necessary for exploration, even when living in the present world. Research exploring the topic of safety on the roads has therefore become increasingly relevant. In this paper, we propose a recognition algorithm based on physiological signals acquired from JINS MEME ES_R smart glasses (electrooculography, acceleration and angular velocity) to classify four commonly encountered road types: city road, highway, housing estate and undeveloped area. Data from 30 drivers were acquired in real driving conditions. Hand-crafted statistical features were extracted from the physiological signals to train and evaluate a random forest classifier. We achieved an overall accuracy, precision, recall and F1 score of 87.64%, 86.30%, 88.12% and 87.08% on the test dataset, respectively.

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