Bi-box Regression for Pedestrian Detection and Occlusion Estimation
一个回归full body，一个回归visible part。 分数融合方式。关键就是中间那个p^1 hat。其实就是把原来的正分相加，负分相加，再做softmax。 对proposal是否是正样本判断的规则改进：要跟visible重合区域够大才算。
相关文章
- 1. CUHK Occlusion Dataset (for pedestrian detection)
- 2. Linear Regression and Maximum Likelihood Estimation
- 3. Caltech Pedestrian Detection Benchmark
- 4. PointSetNet：Point-Set Anchors for Object Detection,Instance Segmentation and Pose Estimation
- 5. Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation
- 6. How Far are We from Solving Pedestrian Detection?
- 7. Object Detection(目标检测神文)(二)
- 8. Deep Learning Models and Code for Pose Estimation
- 9. Pushing the Limits of Deep CNNs for Pedestrian Detection
- 10. Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • RxJava操作符（七）Conditional and Boolean
- • 算法总结-股票买卖
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. CUHK Occlusion Dataset (for pedestrian detection)
- 2. Linear Regression and Maximum Likelihood Estimation
- 3. Caltech Pedestrian Detection Benchmark
- 4. PointSetNet：Point-Set Anchors for Object Detection,Instance Segmentation and Pose Estimation
- 5. Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation
- 6. How Far are We from Solving Pedestrian Detection?
- 7. Object Detection(目标检测神文)(二)
- 8. Deep Learning Models and Code for Pose Estimation
- 9. Pushing the Limits of Deep CNNs for Pedestrian Detection
- 10. Learning Cross-Modal Deep Representations for Robust Pedestrian Detection