计算机视觉 - 是否有必要具有多个分类器具有某些观点的对象检测？ [英] Computer Vision - Is it necessary to have multi classifiers with certain viewpoint for object detection?

问题描述

假设我想训练一个HOG描述符+线性SVM用于汽车检测。我有必要做，让我们说三个分类器，是汽车的后视图，前视图和侧视图，或者我可以训练一个单一的分类器的汽车的所有观点？

Let say I want to train a HOG descriptor + Linear SVM for a car detection. Is it necessary for me to make, let say three classifiers, that are back-view, front-view and side-view of the car or I can just train a single classier for all viewpoints of the car?

推荐答案

这不是必须的，但建议。您可以创建一个单一的分类器来处理多个案例，但它不会执行非常好的整体。这里的问题不在于不同视图之间描述符响应的可变性，而是后/前向和侧向检测器之间的宽高比的差异。您用于提取您的HOG的滑动窗口将捕获太多负面（侧视大小超过后/前面）或没有足够的正数据（后/前面大小过侧面）。

It's not necessary but recommended. You can make a single classifier which handles multiple cases but it won't perform very well overall. The issue here isn't so much the variability of descriptor responses between the different views, but the difference in aspect ratios between rear/front-facing and side-facing detectors. The sliding window you use to extract your HOG will either capture too much negative (sideview-sized over rear/front) or not enough positive data (rear/front-sized over sideview).

底线：这取决于您的准确度/处理速率要求。从我的经验前/后通常是相似的，但如果要求高精度，你需要一个单独的检测器。你肯定需要一个单独的边检测器，你甚至可能需要专门的四分之一视图检测器 - 前右，前左，后右，左后。

Bottom line: it depends on your accuracy/processing rate requirements. From my experience front/rear are usually similar enough, but if high accuracy is desired, you'll need a separate detector for each. You will definitely need a separate side detector and you might even need dedicated 'quarter view' detectors - front right, front left, rear right, rear left.

我忽略了你还需要处理多种尺度的事实。

P.S. I'm omitting the fact that you will also need to handle multiple scales.

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