Pedestrian trajectory prediction is a relevant task for many kinds of intelligent systems. However, it can be quite challenging, since humans can be influenced by a plethora of factors. Two types of factors have been getting more relevance: the presence of obstacles and social interactions. Existing trajectory prediction methods that incorporate both criteria require information like video images, which may not be readily available. We propose a new model, named Arc-LSTM-SMF, which considers the existence of obstacles and social interactions. To our knowledge, it is one of the first methods to include both factors while requiring only pedestrian trajectories as input to accurately perform prediction. This model integrates Sparse Motion Fields with LSTM networks, and introduces a new pooling layer that simulates a field of view for each pedestrian. We evaluate our model using standard geometric metrics, as well as metrics related to obstacle avoidance and pedestrian collision avoidance. The proposed Arc-LSTM-SMF is able to outperform several state-of-the-art models on popular pedestrian datasets.