Pedestrian Detection with Sparse Depth Estimation
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概要
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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 humans 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.
論文 | ランダム
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- 高血圧,動脉硬化を有する糖尿病 (糖尿病(特集))
- 組織検査の現状 (形態検査)
- Insuloma (内分泌異常疾患の臨床(特集))
- 悪性腫瘍細胞診 (塗抹検査(特集))