Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization

Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning

The effect of moving to East Village, the former London 2012 Olympic and Paralympic Games Athletes' Village, on mode of travel (ENABLE London study, a natural experiment)

Background Interventions to encourage active modes of travel (walking, cycling) may improve physical activity levels, but longitudinal evidence is limited and major change in the built environment / travel infrastructure may be needed. East Village (the former London 2012 Olympic Games Athletes Village) has been repurposed on active design principles with improved walkability, open space and public transport and restrictions on residential car parking. We examined the effect of moving to East Village on adult travel patterns. Methods One thousand two hundred seventy-eight adults (16+ years) seeking to move into social, intermediate, and market-rent East Village accommodation were recruited in 2013–2015, and followed up after 2 years. Individual objective measures of physical activity using accelerometry (ActiGraph GT3X+) and geographic location using GPS travel recorders (QStarz) were time-matched and a validated algorithm assigned four travel modes (walking, cycling, motorised vehicle, train). We examined change in time spent in different travel modes, using multilevel linear regresssion models adjusting for sex, age group, ethnicity, housing group (fixed effects) and household (random effect), comparing those who had moved to East Village at follow-up with those who did not. Results Of 877 adults (69%) followed-up, 578 (66%) provided valid accelerometry and GPS data for at least 1 day (≥540 min) at both time points; half had moved to East Village. Despite no overall effects on physical activity levels, sizeable improvements in walkability and access to public transport in East Village resulted in decreased daily vehicle travel (8.3 mins, 95%CI 2.5,14.0), particularly in the intermediate housing group (9.6 mins, 95%CI 2.2,16.9), and increased underground travel (3.9 mins, 95%CI 1.2,6.5), more so in the market-rent group (11.5 mins, 95%CI 4.4,18.6). However, there were no effects on time spent walking or cycling

Visual place recognition using landmark distribution descriptors

Recent work by Suenderhauf et al. [1] demonstrated improved visual place recognition using proposal regions coupled with features from convolutional neural networks (CNN) to match landmarks between views. In this work we extend the approach by introducing descriptors built from landmark features which also encode the spatial distribution of the landmarks within a view. Matching descriptors then enforces consistency of the relative positions of landmarks between views. This has a significant impact on performance. For example, in experiments on 10 image-pair datasets, each consisting of 200 urban locations with significant differences in viewing positions and conditions, we recorded average precision of around 70% (at 100% recall), compared with 58% obtained using whole image CNN features and 50% for the method in [1].Comment: 13 page

Cohort profile: Examining Neighbourhood Activities in Built Living Environments in London: the ENABLE London-Olympic Park cohort.

PURPOSE: The Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) project is a natural experiment which aims to establish whether physical activity and other health behaviours show sustained changes among individuals and families relocating to East Village (formerly the London 2012 Olympics Athletes' Village), when compared with a control population living outside East Village throughout. PARTICIPANTS: Between January 2013 and December 2015, 1497 individuals from 1006 households were recruited and assessed (at baseline) (including 392 households seeking social housing, 421 seeking intermediate and 193 seeking market rent homes). The 2-year follow-up rate is 62% of households to date, of which 57% have moved to East Village. FINDINGS TO DATE: Assessments of physical activity (measured objectively using accelerometers) combined with Global Positioning System technology and Geographic Information System mapping of the local area are being used to characterise physical activity patterns and location among study participants and assess the attributes of the environments to which they are exposed. Assessments of body composition, based on weight, height and bioelectrical impedance, have been made and detailed participant questionnaires provide information on socioeconomic position, general health/health status, well-being, anxiety, depression, attitudes to leisure time activities and other personal, social and environmental influences on physical activity, including the use of recreational space and facilities in their residential neighbourhood. FUTURE PLANS: The main analyses will examine the changes in physical activity, health and well-being observed in the East Village group compared with controls and the influence of specific elements of the built environment on observed changes. The ENABLE London project exploits a unique opportunity to evaluate a 'natural experiment', provided by the building and rapid occupation of East Village. Findings from the study will be generalisable to other urban residential housing developments, and will help inform future evidence-based urban planning

An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data.

BACKGROUND: Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data. METHODS: The Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability. RESULTS: Applying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals' travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%). CONCLUSION: We present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers

Weekend and weekday associations between the residential built environment and physical activity: Findings from the ENABLE London study.

BACKGROUND: We assessed whether the residential built environment was associated with physical activity (PA) differently on weekdays and weekends, and contributed to socio-economic differences in PA. METHODS: Measures of PA and walkability, park proximity and public transport accessibility were derived for baseline participants (n = 1,064) of the Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) Study. Multilevel-linear-regressions examined associations between weekend and weekday steps and Moderate to Vigorous PA (MVPA), residential built environment factors, and housing tenure status as a proxy for socio-economic position. RESULTS: A one-unit decrease in walkability was associated with 135 (95% CI [28; 242]) fewer steps and 1.2 (95% CI [0.3; 2.1]) fewer minutes of MVPA on weekend days, compared with little difference in steps and minutes of MVPA observed on weekdays. A 1km-increase in distance to the nearest local park was associated with 597 (95% CI [161; 1032]) more steps and 4.7 (95% CI [1.2; 8.2]) more minutes of MVPA on weekend days; 84 fewer steps (95% CI [-253;420]) and 0.3 fewer minutes of MVPA (95%CI [-2.3, 3.0]) on weekdays. Lower public transport accessibility was associated with increased steps on a weekday (767 steps, 95%CI [-13,1546]) compared with fewer steps on weekend days (608 fewer steps, 95% CI [-44, 1658]). None of the associations between built environment factors and PA on either weekend or weekdays were modified by socio-economic status. However, socio-economic differences in PA related moderately to socio-economic disparities in PA-promoting features of the residential neighbourhood. CONCLUSIONS: The residential built environment is associated with PA differently at weekends and on weekdays, and contributes moderately to socio-economic differences in PA

Evaluating the effect of change in the built environment on mental health and subjective well-being: a natural experiment

Background Neighbourhood characteristics may affect mental health and well-being, but longitudinal evidence is limited. We examined the effect of relocating to East Village (the former London 2012 Olympic Athletes’ Village), repurposed to encourage healthy active living, on mental health and well-being. Methods 1278 adults seeking different housing tenures in East village were recruited and examined during 2013–2015. 877 (69%) were followed-up after 2 years; 50% had moved to East Village. Analysis examined change in objective measures of the built environment, neighbourhood perceptions (scored from low to high; quality −12 to 12, safety −10 to 10 units), self-reported mental health (depression and anxiety) and well-being (life satisfaction, life being worthwhile and happiness) among East Village participants compared with controls who did not move to East Village. Follow-up measures were regressed on baseline for each outcome with group status as a binary variable, adjusted for age, sex, ethnicity, housing tenure and household clustering (random effect). Results Participants who moved to East Village lived closer to their nearest park (528 m, 95% CI 482 to 575 m), in more walkable areas, and had better access to public transport, compared with controls. Living in East Village was associated with marked improvements in neighbourhood perceptions (quality 5.0, 95% CI 4.5 to 5.4 units; safety 3.4, 95% CI 2.9 to 3.9 units), but there was no overall effect on mental health and well-being outcomes. Conclusion Despite large improvements in the built environment, there was no evidence that moving to East Village improved mental health and well-being. Changes in the built environment alone are insufficient to improve mental health and well-being

Weekend and weekday associations between the residential built environment and physical activity: Findings from the ENABLE London study.

BACKGROUND: We assessed whether the residential built environment was associated with physical activity (PA) differently on weekdays and weekends, and contributed to socio-economic differences in PA. METHODS: Measures of PA and walkability, park proximity and public transport accessibility were derived for baseline participants (n = 1,064) of the Examining Neighbourhood Activities in Built Living Environments in London (ENABLE London) Study. Multilevel-linear-regressions examined associations between weekend and weekday steps and Moderate to Vigorous PA (MVPA), residential built environment factors, and housing tenure status as a proxy for socio-economic position. RESULTS: A one-unit decrease in walkability was associated with 135 (95% CI [28; 242]) fewer steps and 1.2 (95% CI [0.3; 2.1]) fewer minutes of MVPA on weekend days, compared with little difference in steps and minutes of MVPA observed on weekdays. A 1km-increase in distance to the nearest local park was associated with 597 (95% CI [161; 1032]) more steps and 4.7 (95% CI [1.2; 8.2]) more minutes of MVPA on weekend days; 84 fewer steps (95% CI [-253;420]) and 0.3 fewer minutes of MVPA (95%CI [-2.3, 3.0]) on weekdays. Lower public transport accessibility was associated with increased steps on a weekday (767 steps, 95%CI [-13,1546]) compared with fewer steps on weekend days (608 fewer steps, 95% CI [-44, 1658]). None of the associations between built environment factors and PA on either weekend or weekdays were modified by socio-economic status. However, socio-economic differences in PA related moderately to socio-economic disparities in PA-promoting features of the residential neighbourhood. CONCLUSIONS: The residential built environment is associated with PA differently at weekends and on weekdays, and contributes moderately to socio-economic differences in PA