211 research outputs found
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
View-invariant action recognition
Human action recognition is an important problem in computer vision. It has a
wide range of applications in surveillance, human-computer interaction,
augmented reality, video indexing, and retrieval. The varying pattern of
spatio-temporal appearance generated by human action is key for identifying the
performed action. We have seen a lot of research exploring this dynamics of
spatio-temporal appearance for learning a visual representation of human
actions. However, most of the research in action recognition is focused on some
common viewpoints, and these approaches do not perform well when there is a
change in viewpoint. Human actions are performed in a 3-dimensional environment
and are projected to a 2-dimensional space when captured as a video from a
given viewpoint. Therefore, an action will have a different spatio-temporal
appearance from different viewpoints. The research in view-invariant action
recognition addresses this problem and focuses on recognizing human actions
from unseen viewpoints
A 3D Human Posture Approach for Activity Recognition Based on Depth Camera
Human activity recognition plays an important role in the context of Ambient Assisted Living (AAL), providing useful tools to improve people quality of life. This work presents an activity recognition algorithm based on the extraction of skeleton joints from a depth camera. The system describes an activity using a set of few and basic postures extracted by means of the X-means clustering algorithm. A multi-class Support Vector Machine, trained with the Sequential Minimal Optimization is employed to perform the classification. The system is evaluated on two public datasets for activity recognition which have different skeleton models, the CAD-60 with 15 joints and the TST with 25 joints. The proposed approach achieves precision/recall performances of 99.8 % on CAD-60 and 97.2 %/91.7 % on TST. The results are promising for an applied use in the context of AAL
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