Deep Mixture Density Network for Probabilistic Object Detection
| Autor: | Yihui He, Jianren Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Probabilistic logic Pattern recognition 02 engineering and technology 010501 environmental sciences Covariance Mixture model Object (computer science) 01 natural sciences Object detection Minimum bounding box 0202 electrical engineering electronic engineering information engineering Overhead (computing) Mixture distribution 020201 artificial intelligence & image processing Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | IROS |
| DOI: | 10.1109/iros45743.2020.9340882 |
| Popis: | Mistakes/uncertainties in object detection could lead to catastrophes when deploying robots in the real world. In this paper, we measure the uncertainties of object localization to minimize this kind of risk. Uncertainties emerge upon challenging cases like occlusion. The bounding box borders of an occluded object can have multiple plausible configurations. We propose a deep multivariate mixture of Gaussians model for probabilistic object detection. The covariances help to learn the relationship between the borders, and the mixture components potentially learn different configurations of an occluded part. Quantitatively, our model improves the AP of the baselines by 3.9% and 1.4% on CrowdHuman and MS-COCO respectively with almost no computational or memory overhead. Qualitatively, our model enjoys explainability since the resulting covariance matrices and the mixture components help measure uncertainties. IROS 2020 oral |
| Databáze: | OpenAIRE |
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