In an object detection system, the main objective during training is to maintain the detection and false positive rates under acceptable levels when the model is run over the test set. However, this typically translates into an unacceptable rate of false alarms when the system is deployed in a real surveillance scenario. To deal with this situation, which often leads to system shutdown, we propose to add a filter step to discard part of the new false positive detections that are typical of the new scenario. This step consists of a deep autoencoder trained with the false alarm detections generated after running the detector over a period of time in the new scenario. Therefore, this step will be in charge of determining whether the detection is a typical false alarm of that scenario or whether it is something anomalous for the autoencoder and, therefore, a true detection. In order to decide whether a detection must be filtered, three different approaches have been tested. The first one uses the autoencoder reconstruction error measured with the mean squared error to make the decision. The other two use the k-NN (k-nearest neighbors) and one-class SVMs (support vector machines) classifiers trained with the autoencoder vector representation. In addition, a synthetic scenario has been generated with Unreal Engine 4 to test the proposed methods in addition to a dataset with real images. The results obtained show a reduction in the number of false positives between 22.5% and 87.2% and an increase in the system’s precision of 1.2%\(-47\)% when the autoencoder is applied.

在对象检测系统中，训练期间的主要目标是当模型在测试集上运行时，将检测和假阳性率保持在可接受的水平下。但是，当系统部署在实际监视场景中时，这通常会导致错误警报的发生率不可接受。为了应对这种情况，这种情况通常会导致系统关闭，我们建议添加一个过滤步骤，以丢弃部分新场景中常见的新误报检测。此步骤包含一个深度自动编码器，该编码器经过训练，在新方案中运行检测器一段时间后生成虚假警报检测结果。因此，此步骤将负责确定检测是该场景的典型错误警报还是自动编码器的异常情况，以及 因此，是真正的检测。为了决定是否必须过滤检测，已经测试了三种不同的方法。第一种方法是使用自动编码器重构误差和均方误差来进行决策。其他两个使用用自动编码器矢量表示训练的k -NN（k个最近邻居）和一类SVM（支持向量机）分类器。另外，除了具有真实图像的数据集之外，还使用Unreal Engine 4生成了一个综合方案来测试所提出的方法。获得的结果表明，使用自动编码器时，误报的数量减少了22.5％至87.2％，系统的精度提高了1.2％\（-47 \）％。