Blur, eye movements and performance on a driving visual recognition slide test

Purpose Optical blur and ageing are known to affect driving performance but their effects on drivers' eye movements are poorly understood. This study examined the effects of optical blur and age on eye movement patterns and performance on the DriveSafe slide recognition test which is purported to predict fitness to drive. Methods Twenty young (27.1 ± 4.6 years) and 20 older (73.3 ± 5.7 years) visually normal drivers performed the DriveSafe under two visual conditions: best-corrected vision and with +2.00 DS blur. The DriveSafe is a Visual Recognition Slide Test that consists of brief presentations of static, real-world driving scenes containing different road users (pedestrians, bicycles and vehicles). Participants reported the types, relative positions and direction of travel of the road users in each image; the score was the number of correctly reported items (maximum score of 128). Eye movements were recorded while participants performed the DriveSafe test using a Tobii TX300 eye tracking system. Results There was a significant main effect of blur on DriveSafe scores (best-corrected: 114.9 vs blur: 93.2; p < 0.001). There was also a significant age and blur interaction on the DriveSafe scores (p < 0.001) such that the young drivers were more negatively affected by blur than the older drivers (reductions of 22% and 13% respectively; p < 0.001): with best-corrected vision, the young drivers performed better than the older drivers (DriveSafe scores: 118.4 vs 111.5; p = 0.001), while with blur, the young drivers performed worse than the older drivers (88.6 vs 95.9; p = 0.009). For the eye movement patterns, blur significantly reduced the number of fixations on road users (best-corrected: 5.1 vs blur: 4.5; p < 0.001), fixation duration on road users (2.0 s vs 1.8 s; p < 0.001) and saccade amplitudes (7.4° vs 6.7°; p < 0.001). A main effect of age on eye movements was also found where older drivers made smaller saccades than the young drivers (6.7° vs 7.4°; p < 0.001). Conclusions Blur reduced DriveSafe scores for both age groups and this effect was greater for the young drivers. The decrease in number of fixations and fixation duration on road users, as well as the reduction in saccade amplitudes under the blurred condition, highlight the difficulty experienced in performing the task in the presence of optical blur, which suggests that uncorrected refractive errors may have a detrimental impact on aspects of driving performance.

Repeatable Condition-Invariant Visual Odometry for Sequence-Based Place Recognition

This paper describes a vision-only system for place recognition in environments that are tra- versed at different times of day, when chang- ing conditions drastically affect visual appear- ance, and at different speeds, where places aren’t visited at a consistent linear rate. The ma- jor contribution is the removal of wheel-based odometry from the previously presented algo- rithm (SMART), allowing the technique to op- erate on any camera-based device; in our case a mobile phone. While we show that the di- rect application of visual odometry to our night- time datasets does not achieve a level of perfor- mance typically needed, the VO requirements of SMART are orthogonal to typical usage: firstly only the magnitude of the velocity is required, and secondly the calculated velocity signal only needs to be repeatable in any one part of the environment over day and night cycles, but not necessarily globally consistent. Our results show that the smoothing effect of motion constraints is highly beneficial for achieving a locally consis- tent, lighting-independent velocity estimate. We also show that the advantage of our patch-based technique used previously for frame recogni- tion, surprisingly, does not transfer to VO, where SIFT demonstrates equally good performance. Nevertheless, we present the SMART system us- ing only vision, which performs sequence-base place recognition in extreme low-light condi- tions where standard 6-DOF VO fails and that improves place recognition performance over odometry-less benchmarks, approaching that of wheel odometry.

A model for visual recognition of alphanumerics

In this paper we propose a hypothetical scheme for recognizing the alphanumerics. The scheme is based on the known physiological structure of the visual cortex and the concept of a short Lino extractor nouron (SLEN). We assumo four basic typca of such units for extracting vertical, horizontal, right and left inclined straight line segments. The patterns reconstructed from the scheme show perfect agreement with the test patterns. The model indicates that the recognition of letters T and H requires extraction of the largest number of features.