Object recognition for human behavior analysis

Dayangac, Enes; Hirtz, Gangolf

doi:10.1109/icce-berlin.2014.7034218

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…This includes users with multiple and/or severe disabilities who find it difficult to maneuver the wheelchair in typical places where approaches are tightly constrained [ 21 ] either due to various untypical and vivid outdoor settings or users’ lack of autonomy to control. Aside from these requirements on mobility, clinical surveys have also proved the users’ desire to gain leisure, independence, and productivity by benefiting from these technologies [ 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Self-Supervised Sidewalk Perception Using Fast Video Semantic Segmentation for Robotic Wheelchairs in Smart Mobility

Pradeep

Khemmar

Lecrosnier

et al. 2022

Sensors

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The real-time segmentation of sidewalk environments is critical to achieving autonomous navigation for robotic wheelchairs in urban territories. A robust and real-time video semantic segmentation offers an apt solution for advanced visual perception in such complex domains. The key to this proposition is to have a method with lightweight flow estimations and reliable feature extractions. We address this by selecting an approach based on recent trends in video segmentation. Although these approaches demonstrate efficient and cost-effective segmentation performance in cross-domain implementations, they require additional procedures to put their striking characteristics into practical use. We use our method for developing a visual perception technique to perform in urban sidewalk environments for the robotic wheelchair. We generate a collection of synthetic scenes in a blending target distribution to train and validate our approach. Experimental results show that our method improves prediction accuracy on our benchmark with tolerable loss of speed and without additional overhead. Overall, our technique serves as a reference to transfer and develop perception algorithms for any cross-domain visual perception applications with less downtime.

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Section: Related Workmentioning

confidence: 99%