As the increasing number of people watching mobile phones while walking, sidewalk accidents occur more frequently. Using mobile camera instead of distracted pedestrians to monitor the road conditions ahead can effectively prevent pedestrian accidents. In order to develop this kind of applications, a widefield sidewalk dataset becomes a necessity. In this paper, three major contributions are concluded. Firstly, a dataset quality evaluation model is proposed, which directs the establishment of a wide-view dataset named PESID for the sidewalk environment. PESID currently contains more than 1.9K labeled images which cover more than 5 districts, 10 communal facilities, 6 typical roads. Secondly, a criterion is presented to evaluate 9 up-to-date object detection algorithms in order to train a mobile feasible obstacle detection model. Finally, a reliable and low-cost framework is designed for obstacle detection based pedestrian safety application. The proposal is able to avoid 71.4% collisions on average by evaluating the mean average precision (MAP), the dangerous reminder omission rate and the false detection rate of the model.

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