HopInAndAction: a benchmark for action recognition in the cockpit of a self-driving car driving outdoors - Archive ouverte HAL
Proceedings/Recueil Des Communications Année : 2024

HopInAndAction: a benchmark for action recognition in the cockpit of a self-driving car driving outdoors

Résumé

Self-driving cars (SDC) are already present in certain cities of the world as part of robot taxi services. A few self-driving capabilities are also becoming more frequent in high-end consumer vehicles. But even though several datasets exist to develop methods that can enable a car to “see”, few datasets depict the actions of the occupants of an SDC in outdoor conditions. This work proposes HopInAndAction, a public dataset to evaluate action classification methods on videos of people realizing non-driving activities in the cockpit of an SDC. The dataset contains RGB recordings of 31 people carrying out 19 action classes based on 7 daily objects (e.g., telephone, newspaper, or tablet). Recordings are done in a vehicle driving autonomously in outdoor conditions and illustrate a varied set of monitoring conditions. For instance, strong and varying illumination and action occlusions due to sub-optimal sensor placement or self-body occlusion. Preliminary experiments show that HopInAndAction presents challenging monitoring conditions to the evaluated methods, as they struggle to discern between actions with similar appearances or to identify a person-object interaction in a scene with multiple objects.
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Dates et versions

hal-04844333 , version 1 (17-12-2024)

Identifiants

Citer

Tristan Alex-Garnier, Romain Guesdon, Laure Tougne-Rodet, Carlos Fernando Crispim-Junior. HopInAndAction: a benchmark for action recognition in the cockpit of a self-driving car driving outdoors. 2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp.1-7, 2024, ⟨10.1109/AVSS61716.2024.10672571⟩. ⟨hal-04844333⟩
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