Survey on Vision-based Path Prediction
It provides a comprehensive overview for researchers in computer vision and autonomous systems, but is incremental as it synthesizes existing work.
This survey systematically summarizes vision-based path prediction methods that use video input to estimate pedestrian or vehicle movements, and introduces datasets for quantitative evaluation.
Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.