Toward Vehicle Occupant-Invariant Models for Activity Characterization

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Capozzi, L (Autor) Outra; Barbosa, V (Autor) Outra; Pinto, C (Autor) Outra; Pinto, JR (Autor) Outra; Pereira, A (Autor) Outra; Pedro Carvalho (Autor) Outra; Jaime S Cardoso (Autor) FEUP

Revista

Título: IEEE Access

Vol. 10

ISSN: 2169-3536

Editora: IEEE

Indexação

ISI Web of Knowledge - 1 Citação

Outras Informações

ID Authenticus: P-00X-87A

DOI: 10.1109/access.2022.3210973

Abstract (EN): With the advent of self-driving cars and the push by large companies into fully driverless transportation services, monitoring passenger behaviour in vehicles is becoming increasingly important for several reasons, such as ensuring safety and comfort. Although several human action recognition (HAR) methods have been proposed, developing a true HAR system remains a very challenging task. If the dataset used to train a model contains a small number of actors, the model can become biased towards these actors and their unique characteristics. This can cause the model to generalise poorly when confronted with new actors performing the same actions. This limitation is particularly acute when developing models to characterise the activities of vehicle occupants, for which data sets are short and scarce. In this study, we describe and evaluate three different methods that aim to address this actor bias and assess their performance in detecting in-vehicle violence. These methods work by removing specific information about the actor from the model's features during training or by using data that is independent of the actor, such as information about body posture. The experimental results show improvements over the baseline model when evaluated with real data. On the Hanau03 Vito dataset, the accuracy improved from 65.33% to 69.41%. On the Sunnyvale dataset, the accuracy improved from 82.81% to 86.62%.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11