Different aspects of the built and natural environment appear related to people's walking behavior. State-of-the-art transport studies typically incorporate built environmental measures (e.g., density, diversity, design). However, street view (SV) environments capturing how pedestrians perceived their surroundings on site are understudied. Therefore, this study examined possibly non-linear associations between multiple SV-derived environmental features and pedestrians’ walking duration in Amsterdam, the Netherlands. We used travel survey data (N = 1,886) between 2014 and 2017. SV-derived environmental measures (e.g., cars and people) were extracted from SV images through a fully convolutional neural network. Covariate-adjusted generalized additive mixed models were fitted to the data. Our results showed that walking-SV features associations differed between weekdays and weekends. On weekdays, pedestrians walked more in neighborhoods with fewer individual standing walls and lower address density. On weekends, pedestrians’ walking duration increased with more street greenery, fewer cars, higher address density, pronounced land-use diversity, and further distances to train stations. Non-linear associations were found only in the case of weekday SV-derived people, even after adjusting for other neighborhood characteristics (e.g., address density, land-use mix, and street connectivity). Our findings suggest that SV environmental features complement the typically used built environmental measures to explain pedestrians’ mobility. Policy-makers and urban planners are advised to incorporate characteristics of the street environments, also should not only rely on the conventional thinking “the more, the merrier”.