Name
Abstract | ||
|---|---|---|
In this paper, we deal with the pedestrian detection task in outdoor scenes. Because of the complexity of such scenes, generally used gradient-feature-based detectors do not work well on them. We propose to use sparse 3D depth information as an additional cue to do the detection task, in order to achieve a fast improvement in performance. Our proposed method uses a probabilistic model to integrate image-feature-based classification with sparse depth estimation. Benefiting from the depth estimates, we map the prior distribution of human's actual height onto the image, and update the image-feature-based classification result probabilistically. We have two contributions in this paper: 1) a simplified graphical model which can efficiently integrate depth cue in detection; and 2) a sparse depth estimation method which could provide fast and reliable estimation of depth information. An experiment shows that our method provides a promising enhancement over baseline detector within minimal additional time. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1587/transinf.E94.D.1690 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
pedestrian detection, depth estimation, stereo matching | Stereo matching,Computer vision,Pattern recognition,Computer science,Statistical model,Artificial intelligence,Graphical model,Prior probability,Detector,Pedestrian detection | Journal |
Volume | Issue | ISSN |
E94D | 8 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
2 | ||