12,386 research outputs found
Online real-time crowd behavior detection in video sequences
Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach
Survey on Vision-based Path Prediction
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.Comment: DAPI 201
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