Online Observability-Constrained Motion Suggestion via Efficient Motion Primitive-Based Observability Analysis

An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.

Geometry of Motion for Video Shakiness Detection

This paper presents a novel algorithm for automatically detecting global shakiness in casual videos. Per-frame amplitude is computed by the geometry of motion, based on the kinematic model defined by inter-frame geometric transformations. Inspired by motion perception, we investigate the just-noticeable amplitude of shaky motion perceived by the human visual system. Then, we use the thresholding contrast strategy on the statistics of per-frame amplitudes to determine the occurrence of perceived shakiness. For testing the detection accuracy, a dataset of video clips is constructed with manual shakiness label as the ground truth. The experiments demonstrate that our algorithm can obtain good detection accuracy that is in concordance with subjective judgement on the videos in the dataset.

Multiplicative Inhibitory Velocity Detector and Multi-Velocity Motion Detection NeuralNetwork Model

Motion perception is one of the most important aspects of the biological visual system, from which people get a lot of information of the natural world. In this paper, trying to simulate the neurons in MT (motion area in visual cortex) which respond selectively both in direction and speed, the authors propose a novel multiplicative inhibitory velocity detector (MIVD) model, whose spatiotemporal joint parameter K determines its optimal velocity. Based on the Response Amplitude Disparity (RAD) property of MIVD, two multi-velocity fusion neural networks (a simple one and an active one) are built to detect the velocity of 1-Dimension motion. The experiments show that the active MIVD Neural Network with a feedback fusion method has a relatively better result.

Form Properties of Moving Targets Bias Smooth Pursuit Target Selection in Monkeys

During natural viewing,we often recognize multiple objects,detect their motion,and select one object as the target to track.It remains to be determined how such behavior is guided by the integration of visual form and motion perception.To address this,we studied how monkeys made a choice to track moving targets with different forms by smooth pursuit eye movements in a two-target task.We found that pursuit responses were biased toward the motion direction of a target with a hole.By computing the relative weighting,we found that the target with a hole exhibited a larger weight for vector computation.The global hole feature dominated other form properties.This dominance failed to account for changes in pursuit responses to a target with different forms moving singly.These findings suggest that the integration of visual form and motion perception can reshape the competition in sensorimotor networks to guide behavioral selection.