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pro vyhledávání: '"Wong, Conghao"'
Trajectory prediction is a crucial aspect of understanding human behaviors. Researchers have made efforts to represent socially interactive behaviors among pedestrians and utilize various networks to enhance prediction capability. Unfortunately, they
Externí odkaz:
http://arxiv.org/abs/2409.14984
Analyzing and forecasting trajectories of agents like pedestrians and cars in complex scenes has become more and more significant in many intelligent systems and applications. The diversity and uncertainty in socially interactive behaviors among a ri
Externí odkaz:
http://arxiv.org/abs/2310.05370
With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more heterogeneous trajectories with different representation forms, such as 2D or 3D coor
Externí odkaz:
http://arxiv.org/abs/2304.05106
Trajectory prediction aims to predict the movement trend of the agents like pedestrians, bikers, vehicles. It is helpful to analyze and understand human activities in crowded spaces and widely applied in many areas such as surveillance video analysis
Externí odkaz:
http://arxiv.org/abs/2202.08506
Autor:
Wong, Conghao, Xia, Beihao, Hong, Ziming, Peng, Qinmu, Yuan, Wei, Cao, Qiong, Yang, Yibo, You, Xinge
Understanding and forecasting future trajectories of agents are critical for behavior analysis, robot navigation, autonomous cars, and other related applications. Previous methods mostly treat trajectory prediction as time sequence generation. Differ
Externí odkaz:
http://arxiv.org/abs/2110.07288
Trajectory prediction aims to forecast agents' possible future locations considering their observations along with the video context. It is strongly needed by many autonomous platforms like tracking, detection, robot navigation, and self-driving cars
Externí odkaz:
http://arxiv.org/abs/2107.00932
Visual images usually contain the informative context of the environment, thereby helping to predict agents' behaviors. However, they hardly impose the dynamic effects on agents' actual behaviors due to the respectively fixed semantics. To solve this
Externí odkaz:
http://arxiv.org/abs/2010.03897
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