Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Anastasiu, David"'
Autor:
Kong, Quan, Kawana, Yuki, Saini, Rajat, Kumar, Ashutosh, Pan, Jingjing, Gu, Ta, Ozao, Yohei, Opra, Balazs, Anastasiu, David C., Sato, Yoichi, Kobori, Norimasa
In this paper, we address the challenge of fine-grained video event understanding in traffic scenarios, vital for autonomous driving and safety. Traditional datasets focus on driver or vehicle behavior, often neglecting pedestrian perspectives. To fi
Externí odkaz:
http://arxiv.org/abs/2407.15350
Autor:
Wang, Shuo, Anastasiu, David C., Tang, Zheng, Chang, Ming-Ching, Yao, Yue, Zheng, Liang, Rahman, Mohammed Shaiqur, Arya, Meenakshi S., Sharma, Anuj, Chakraborty, Pranamesh, Prajapati, Sanjita, Kong, Quan, Kobori, Norimasa, Gochoo, Munkhjargal, Otgonbold, Munkh-Erdene, Alnajjar, Fady, Batnasan, Ganzorig, Chen, Ping-Yang, Hsieh, Jun-Wei, Wu, Xunlei, Pusegaonkar, Sameer Satish, Wang, Yizhou, Biswas, Sujit, Chellappa, Rama
The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition f
Externí odkaz:
http://arxiv.org/abs/2404.09432
In the hydrology field, time series forecasting is crucial for efficient water resource management, improving flood and drought control and increasing the safety and quality of life for the general population. However, predicting long-term streamflow
Externí odkaz:
http://arxiv.org/abs/2312.08763
Autor:
Naphade, Milind, Wang, Shuo, Anastasiu, David C., Tang, Zheng, Chang, Ming-Ching, Yao, Yue, Zheng, Liang, Rahman, Mohammed Shaiqur, Arya, Meenakshi S., Sharma, Anuj, Feng, Qi, Ablavsky, Vitaly, Sclaroff, Stan, Chakraborty, Pranamesh, Prajapati, Sanjita, Li, Alice, Li, Shangru, Kunadharaju, Krishna, Jiang, Shenxin, Chellappa, Rama
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential. The 2023 challenge
Externí odkaz:
http://arxiv.org/abs/2304.07500
Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction. Diverse traditional and
Externí odkaz:
http://arxiv.org/abs/2211.15891
Autor:
Naphade, Milind, Wang, Shuo, Anastasiu, David C., Tang, Zheng, Chang, Ming-Ching, Yao, Yue, Zheng, Liang, Rahman, Mohammed Shaiqur, Venkatachalapathy, Archana, Sharma, Anuj, Feng, Qi, Ablavsky, Vitaly, Sclaroff, Stan, Chakraborty, Pranamesh, Li, Alice, Li, Shangru, Chellappa, Rama
The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence: Intelligent Traffic Systems (ITS), and brick and
Externí odkaz:
http://arxiv.org/abs/2204.10380
Autor:
Rahman, Mohammed Shaiqur, Wang, Jiyang, Gursoy, Senem Velipasalar, Anastasiu, David, Wang, Shuo, Sharma, Anuj
This article presents a synthetic distracted driving (SynDD2 - a continuum of SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones. We collected the data in a stationary vehic
Externí odkaz:
http://arxiv.org/abs/2204.08096
Autor:
Naphade, Milind, Wang, Shuo, Anastasiu, David C., Tang, Zheng, Chang, Ming-Ching, Yang, Xiaodong, Yao, Yue, Zheng, Liang, Chakraborty, Pranamesh, Lopez, Christian E., Sharma, Anuj, Feng, Qi, Ablavsky, Vitaly, Sclaroff, Stan
The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2) assessing tasks where the level of performance is enough to cause rea
Externí odkaz:
http://arxiv.org/abs/2104.12233
Autor:
Naphade, Milind, Wang, Shuo, Anastasiu, David, Tang, Zheng, Chang, Ming-Ching, Yang, Xiaodong, Zheng, Liang, Sharma, Anuj, Chellappa, Rama, Chakraborty, Pranamesh
The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. Transportation is one of the largest segments that can benefit from actionable insights derived from data captured by sensors, where
Externí odkaz:
http://arxiv.org/abs/2004.14619
Autor:
Tang, Zheng, Naphade, Milind, Liu, Ming-Yu, Yang, Xiaodong, Birchfield, Stan, Wang, Shuo, Kumar, Ratnesh, Anastasiu, David, Hwang, Jenq-Neng
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours
Externí odkaz:
http://arxiv.org/abs/1903.09254