Integrate Sparse Depth Information into Pedestrians Detection

In this paper, we propose to integrate sparse 3D depth information into pedestrian detection task, in order to achieve a fast boost in performance. Our proposed method uses a probabilistic way to integrate image-feature-based detection and sparse depth estimation together. The depth information is used as a cue, and provides additional discriminative ability for the detection. There are two contributions in this paper: 1) a simplified graphical model which could efficiently integrate depth cue into detection; and 2) a sparse depth estimation method which could provide fast and reliable estimation of depth information. The experiment shows that our method could provide promising enhancement over baseline detector with minimal additional time.