Big-five personality factors affecting pedestrian crossing behaviours

Human personality is an important component of psychological factors affecting pedestrian crossing. This paper reports a questionnaire survey on the effects of pedestrian personalities (including neuroticism, extraversion, openness, agreeableness and conscientiousness) on pedestrian violation in China. 675 feedbacks were obtained, of which 535 samples were valid for analysis. The results of the hierarchical regression analysis showed that educational level had significant effect on violation; agreeableness had significant effect on violation, conditional compliance and unconditional compliance; consciousness had significant effect on violation and conditional compliance; extraversion had significant effect on unconditional compliance; neuroticism had significant effect on violation; educational level had significant effect on violation. The results implied that psychological measures played a very important role in pedestrian safety.

Identifying customer behaviour and dwell time using soft biometrics

In a commercial environment, it is advantageous to know how long it takes customers to move between different regions, how long they spend in each region, and where they are likely to go as they move from one location to another. Presently, these measures can only be determined manually, or through the use of hardware tags (i.e. RFID). Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. They include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. While these traits cannot provide robust authentication, they can be used to provide identification at long range, and aid in object tracking and detection in disjoint camera networks. In this chapter we propose using colour, height and luggage soft biometrics to determine operational statistics relating to how people move through a space. A novel average soft biometric is used to locate people who look distinct, and these people are then detected at various locations within a disjoint camera network to gradually obtain operational statistics

Adoption of low-cost rail level crossing warning devices : an Australian case study

The objective of this chapter is to provide rail practitioners with a practical approach for determining safety requirements of low-cost level crossing warning devices (LCLCWDs) on an Australian railway by way of a case study. LCLCWDs, in theory, allow railway operators to improve the safety of passively controlled crossing by upgrading a larger number of level crossings with the same budget that would otherwise be used to upgrade these using the conventional active level crossing control technologies, e.g. track circuit initiated flashing light systems. The chapter discusses the experience and obstacles of adopting LCLCWDs in Australia, and demonstrates how the risk-based approach may be used to make the case for LCLCWDs.

Activity analysis in complicated scenes using DFT coefficients of particle trajectories

Modelling activities in crowded scenes is very challenging as object tracking is not robust in complicated scenes and optical flow does not capture long range motion. We propose a novel approach to analyse activities in crowded scenes using a “bag of particle trajectories”. Particle trajectories are extracted from foreground regions within short video clips using particle video, which estimates long range motion in contrast to optical flow which is only concerned with inter-frame motion. Our applications include temporal video segmentation and anomaly detection, and we perform our evaluation on several real-world datasets containing complicated scenes. We show that our approaches achieve state-of-the-art performance for both tasks.

Unusual scene detection using distributed behaviour model and sparse representation

The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.

Predictors of young people’s charitable intentions to donate money : an extended theory of planned behavior perspective

An extended theory of planned behavior (TPB) was used to predict young people’s intentions to donate money to charities in the future. Students (N = 210; 18-24 years) completed a questionnaire assessing their attitude, subjective norm, perceived behavioral control [PBC], moral obligation, past behavior and intentions toward donating money. Regression analyses revealed the extended TPB explained 61% of the variance in intentions to donate money. Attitude, PBC, moral norm, and past behavior predicted intentions, representing future targets for charitable giving interventions.

Mothers' sleep and driving in the postpartum period

Introduction: Previous studies investigating mothers’ sleep in the postpartum period commonly demonstrated elevated levels of sleepiness in this population. A Karolinska Sleepiness Scale (KSS) rating of 5 or above is associated with an exponential increase in vehicle crash risk. To date, no studies have investigated the relationship between mothers’ sleep in the postpartum period and their driving behaviour. Methods: Sleep-wake diary data was collected from 14 mother-infant dyads during two 7-day assessment periods when the infants were 6 and 12 weeks old. The mothers’ indicated all driving episodes during these weeks and their respective sleepiness level using the KSS. Semi-structured interviews were conducted with the mothers when their infant was 12 weeks old. Results: The infants slept significantly more than their mothers at 6 weeks and 12 weeks of age. During both time points, mothers and infants had a similar number of night awakenings (waking between 22:00 and 06:00), with some mothers experiencing greater than 19 awakenings over 7 nights. Notably, 36% of the mothers did not experience a continuous sleep period longer than 4.5 hours when their infant was 6 weeks old. A total of 141 driving episodes were reported during the 7 day assessment period when the infants were 6 weeks old. Over 50% of the driving episodes were denoted with a KSS score of 5 or above. Strategies mothers cited they employed during this period included only driving when feeling alert, postponing driving until another person is present, and driving in the morning when less sleepy. Conclusion: Mothers are experiencing disrupted sleep at night and some mothers do not obtain more than 4.5 hours of continuous sleep during the early postpartum weeks. In this sample, some mothers reported self-regulating driving behaviour, however over half of the driving episodes were undertaken with a sleepiness rating linked with elevated crash risk.

Effects of Mobile Phone Distraction at the Onset of Amber Light: Analysis of Driving Simulator Data

Distraction whilst driving on an approach to a signalized intersection is particularly dangerous, as potential vehicular conflicts and resulting angle collisions tend to be severe. This study examines the decisions of distracted drivers during the onset of amber lights. Driving simulator data were obtained from a sample of 58 drivers under baseline and handheld mobile phone conditions at the University of IOWA - National Advanced Driving Simulator. Explanatory variables include age, gender, cell phone use, distance to stop-line, and speed. An iterative combination of decision tree and logistic regression analyses are employed to identify main effects, non-linearities, and interactions effects. Results show that novice (16-17 years) and younger (18-25 years) drivers’ had heightened amber light running risk while distracted by cell phone, and speed and distance thresholds yielded significant interaction effects. Driver experience captured by age has a multiplicative effect with distraction, making the combined effect of being inexperienced and distracted particularly risky. Solutions are needed to combat the use of mobile phones whilst driving.

Non-combinatorial library screening reveals subsite cooperativity and identifies new high-efficiency substrates for kallikrein-related peptidase 14

An array of substrates link the tryptic serine protease, kallikrein-related peptidase 14 (KLK14), to physiological functions including desquamation and activation of signaling molecules associated with inflammation and cancer. Recognition of protease cleavage sequences is driven by complementarity between exposed substrate motifs and the physicochemical signature of an enzyme's active site cleft. However, conventional substrate screening methods have generated conflicting subsite profiles for KLK14. This study utilizes a recently developed screening technique, the sparse matrix library, to identify five novel high-efficiency sequences for KLK14. The optimal sequence, YASR, was cleaved with higher efficiency (k(cat)/K(m)=3.81 ± 0.4 × 10(6) M(-1) s(-1)) than favored substrates from positional scanning and phage display by 2- and 10-fold, respectively. Binding site cooperativity was prominent among preferred sequences, which enabled optimal interaction at all subsites as indicated by predictive modeling of KLK14/substrate complexes. These simulations constitute the first molecular dynamics analysis of KLK14 and offer a structural rationale for the divergent subsite preferences evident between KLK14 and closely related KLKs, KLK4 and KLK5. Collectively, these findings highlight the importance of binding site cooperativity in protease substrate recognition, which has implications for discovery of optimal substrates and engineering highly effective protease inhibitors.

In the pursuit of effective affective computing : the relationship between features and registration

For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

The relationship between cognitive ability, insight and self-regulatory behaviors: Findings from the older driver population

Self-regulation is often promoted as a coping strategy that may allow older drivers to drive safely for longer. Self-regulation depends upon drivers making an accurate assessment of their own ability and having a willingness to practice self-regulatory behaviors to compensate for changes in ability. The current study explored the relationship between older drivers’ cognitive ability, their driving confidence and their use of self-regulation. An additional study aim was to explore the relationship between these factors and older drivers’ interest in driving programs. Seventy Australian drivers aged 65 years and over completed a questionnaire about their driving and a brief screening measure of cognitive ability (an untimed Clock Drawing Test). While all participants reported high levels of confidence regarding their driving ability, and agreed that they would continue driving in the foreseeable future, a notable proportion performed poorly on the Clock Drawing Test. Compared to older drivers who successfully completed the Clock Drawing Test, those who failed the cognitive test were significantly less likely to report driving self-regulation, and showed significantly less interest in being involved in driving programs. Older drivers with declining cognitive abilities may not be self-regulating their driving. This group also appears to be unlikely to self-refer to driving programs.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.