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Joint Audio-Visual Idling Vehicle Detection with Streamlined Input Dependencies

Autor: Li, Xiwen, Mohammed, Rehman, Mangin, Tristalee, Saha, Surojit, Whitaker, Ross T, Kelly, Kerry E., Tasdizen, Tolga

Idling vehicle detection (IVD) can be helpful in monitoring and reducing unnecessary idling and can be integrated into real-time systems to address the resulting pollution and harmful products. The previous approach [13], a non-end-to-end model, requ

Externí odkaz: http://arxiv.org/abs/2410.21170

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Vital Insight: Assisting Experts' Sensemaking Process of Multi-modal Personal Tracking Data Using Visualization and LLM

Autor: Li, Jiachen, Steinberg, Justin, Li, Xiwen, Choube, Akshat, Yao, Bingsheng, Wang, Dakuo, Mynatt, Elizabeth, Mishra, Varun

Researchers have long recognized the socio-technical gaps in personal tracking research, where machines can never fully model the complexity of human behavior, making it only able to produce basic rule-based outputs or "black-box" results that lack c

Externí odkaz: http://arxiv.org/abs/2410.14879

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Real-Time Idling Vehicles Detection using Combined Audio-Visual Deep Learning

Autor: Li, Xiwen, Mangin, Tristalee, Saha, Surojit, Blanchard, Evan, Tang, Dillon, Poppe, Henry, Searle, Nathan, Choi, Ouk, Kelly, Kerry, Whitaker, Ross

Combustion vehicle emissions contribute to poor air quality and release greenhouse gases into the atmosphere, and vehicle pollution has been associated with numerous adverse health effects. Roadways with extensive waiting and/or passenger drop off, s

Externí odkaz: http://arxiv.org/abs/2305.14579

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