1,006 research outputs found
Towards Detection of Human Motion
Detecting humans in images is a useful application
of computer vision. Loose and textured clothing, occlusion
and scene clutter make it a difficult problem because
bottom-up segmentation and grouping do not always work.
We address the problem of detecting humans from their motion
pattern in monocular image sequences; extraneous motions
and occlusion may be present. We assume that we may
not rely on segmentation, nor grouping and that the vision
front-end is limited to observing the motion of key points
and textured patches in between pairs of frames. We do not
assume that we are able to track features for more than two
frames. Our method is based on learning an approximate
probabilistic model of the joint position and velocity of different body features. Detection is performed by hypothesis testing on the maximum a posteriori estimate of the pose and motion of the body. Our experiments on a dozen of walking sequences indicate that our algorithm is accurate
and efficient
A Delay Compensation Framework Based on Eye-Movement for Teleoperated Ground Vehicles
An eye-movement-based predicted trajectory guidance control (ePTGC) is
proposed to mitigate the maneuverability degradation of a teleoperated ground
vehicle caused by communication delays. Human sensitivity to delays is the main
reason for the performance degradation of a ground vehicle teleoperation
system. The proposed framework extracts human intention from eye-movement.
Then, it combines it with contextual constraints to generate an
intention-compliant guidance trajectory, which is then employed to control the
vehicle directly. The advantage of this approach is that the teleoperator is
removed from the direct control loop by using the generated trajectories to
guide vehicle, thus reducing the adverse sensitivity to delay. The delay can be
compensated as long as the prediction horizon exceeds the delay. A
human-in-loop simulation platform is designed to evaluate the teleoperation
performance of the proposed method at different delay levels. The results are
analyzed by repeated measures ANOVA, which shows that the proposed method
significantly improves maneuverability and cognitive burden at large delay
levels (>200 ms). The overall performance is also much better than the PTGC
which does not employ the eye-movement feature.Comment: 9 pages, 11 figure
Research on the Influence of Pinduoduo Group-Buying Mode on Consumers\u27 Impulse Buying
Social commerce, with its unique social attributes, has promoted the group-buying mode to become an important marketing tool. Based on the stimulus-organism-response (S-O-R) paradigm, this study constructs a research model of the impact of the social e-commerce platform Pinduoduo group-buying mode on impulse buying, and uses structural equation model (SEM) for empirical analysis. The research results show that under the group-buying mode: perceived price fairness and reciprocity have significant positive influence on satisfaction; source credibility and similarity have positive effects on trust. Trust is positively correlated with satisfaction. Trust significantly affects impulsive buying impulse, but satisfaction has no significant impact on impulsive buying impulse
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