Angular Scale Expansion Theory And The Misperception Of Egocentric Distance In Locomotor Space

Perception is crucial for the control of action, but perception need not be scaled accurately to produce accurate actions. This paper reviews evidence for an elegant new theory of locomotor space perception that is based on the dense coding of angular declination so that action control may be guided by richer feedback. The theory accounts for why so much direct-estimation data suggests that egocentric distance is underestimated despite the fact that action measures have been interpreted as indicating accurate perception. Actions are calibrated to the perceived scale of space and thus action measures are typically unable to distinguish systematic (e.g., linearly scaled) misperception from accurate perception. Whereas subjective reports of the scaling of linear extent are difficult to evaluate in absolute terms, study of the scaling of perceived angles (which exist in a known scale, delimited by vertical and horizontal) provides new evidence regarding the perceptual scaling of locomotor space

Phenomenal regression to the real object in physical and virtual worlds

© 2014, Springer-Verlag London. In this paper, we investigate a new approach to comparing physical and virtual size and depth percepts that captures the involuntary responses of participants to different stimuli in their field of view, rather than relying on their skill at judging size, reaching or directed walking. We show, via an effect first observed in the 1930s, that participants asked to equate the perspective projections of disc objects at different distances make a systematic error that is both individual in its extent and comparable in the particular physical and virtual setting we have tested. Prior work has shown that this systematic error is difficult to correct, even when participants are knowledgeable of its likelihood of occurring. In fact, in the real world, the error only reduces as the available cues to depth are artificially reduced. This makes the effect we describe a potentially powerful, intrinsic measure of VE quality that ultimately may contribute to our understanding of VE depth compression phenomena

Ego-Downward and Ambient Video based Person Location Association

Using an ego-centric camera to do localization and tracking is highly needed for urban navigation and indoor assistive system when GPS is not available or not accurate enough. The traditional hand-designed feature tracking and estimation approach would fail without visible features. Recently, there are several works exploring to use context features to do localization. However, all of these suffer severe accuracy loss if given no visual context information. To provide a possible solution to this problem, this paper proposes a camera system with both ego-downward and third-static view to perform localization and tracking in a learning approach. Besides, we also proposed a novel action and motion verification model for cross-view verification and localization. We performed comparative experiments based on our collected dataset which considers the same dressing, gender, and background diversity. Results indicate that the proposed model can achieve $18.32 \%$ improvement in accuracy performance. Eventually, we tested the model on multi-people scenarios and obtained an average $67.767 \%$ accuracy