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首页> 外文会议>2015 IEEE 1st International Workshop on Consumer Electronics >Training an object detector using only positive samples
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Training an object detector using only positive samples

机译：仅使用正样本训练对象检测器

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摘要

Accurate pedestrian detection has an important role in automotive applications because, by issuing warnings to the driver and acting actively on the car brakes, it can save human lives and decrease the probability of injuries. In order to achieve adequate accuracy, detectors require training sets containing a very large number of negative samples, which can be challenging for the training algorithms of models like support vector machines (SVM). A common approach to deal with such large datasets is Hard Negative Mining (HNM), which avoids working on the full set by growing an active pool of mined samples. A more recent method is the Block-Circulant Decomposition, which achieves the accuracy of HNM at a lower computational cost by reformulating the problem in the Fourier domain. The method however results in additional memory, required during training by the FFT transform, which could be reduced significantly by using only the positive examples. To address the problem, this paper proposes two main contributions: (1) it shows that the circulant decomposition method works with the same performances when only the positive samples are used in the training phase (2) it compares the performance of a detection pipeline based on HOG features trained with either both all negative and positive samples or with only positive samples on the INRIA pedestrian dataset.
机译：准确的行人检测在汽车应用中具有重要作用，因为通过向驾驶员发出警告并主动作用于汽车制动器，可以挽救生命并减少受伤的可能性。为了获得足够的精度，检测器需要包含非常多的负样本的训练集，这对于诸如支持向量机（SVM）之类的模型的训练算法可能具有挑战性。处理此类大型数据集的一种常用方法是“硬负片采矿”（HNM），它可以通过增加一个活动的开采样本池来避免对整个集合进行操作。较新的方法是块循环分解，它通过在傅立叶域中重新构建问题，从而以较低的计算成本实现了HNM的精度。但是，该方法会导致在FFT变换训练过程中需要额外的内存，而仅通过使用正例可以大大减少内存。为了解决这个问题，本文提出了两个主要的贡献：（1）表明循环训练方法在训练阶段仅使用正样本时具有相同的性能（2）比较检测管道的性能。在HOG特征上使用INRIA行人数据集上的所有负样本和正样本或仅用正样本进行训练。

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