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