Deep autoencoder for false positive reduction in handgun detection
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ORIGINAL ARTICLE
Deep autoencoder for false positive reduction in handgun detection Noelia Vallez1
•
Alberto Velasco-Mata1 • Oscar Deniz1
Received: 24 September 2019 / Accepted: 11 September 2020 Ó The Author(s) 2020
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. Keywords Handgun detection False positive reduction Autoencoder One-class classification
1 Introduction Weapons, among other threats, need to be detected as soon as possible to eliminate or mitigate the danger they could cause [1]. Traditionally, the surveillance of public scenarios has been accomplished by the human supervision of the images captured by closed-circuit television (CCTV) systems. However, even an experienced guard may miss a dangerous event due to fatigue or loss of attention [2]. To help with this situation, the creation of automated surveillance systems (AVSs) able to locate potentially threatening objects (or other events) in video has been studied during the last decades [3]. Similarly to other areas, with the introduction of the new deep learning methods these frameworks have obtained
& Noelia Vallez [email protected] 1
VISILAB, University of Castilla La-Mancha, ETSI Industriales, Av. Camilo Jose Cela SN, 13071 Ciudad Real, Spain
promising results and are closer to be used in real scenarios [4, 5]. Nevertheless, although those detectors have high detection (D) and low false positive (FP) rates, when they are used in a different scenario from the one used f
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