Abstract
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.
Highlights
Weapons, among other threats, need to be detected as soon as possible to eliminate or mitigate the danger they could cause [1]
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
Machine with 2 nVIDIA Quadro M4000 cards, Keras with TensorFlow backend and CUDA 8.0 were used to perform the training. After obtaining this base detector, both datasets were divided into 4 parts to (1) train and (2) validate the autoencoder, (3) fit the k-nearest neighbor (NN) and support vector machine (SVM) classifiers, and (4) test the system variants (Fig. 6)
Summary
Among other threats, need to be detected as soon as possible to eliminate or mitigate the danger they could cause [1]. After running the detector in a new scenario, it is possible to collect all the detector alarms All of these alarms are false positives since the incidence of the true event (a handgun in the scene) is very low. All of these detections can be stored and used to model the new scenario. The synthetic scenario resembles a school hallway from the point of view of a surveillance camera This allow us to generate as much data as needed with and without handguns to train and test the autoencoder.
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