Abstract
Recently, video surveillance systems have been perceived as important technical tools that play a fundamental role in protecting people and assets. In particular, the recorded surveillance video sequences are used as evidence to solve violation, theft and criminal cases. Therefore, the identification of the person present on the crime scene becomes a critical task. In this paper, we proposed a Deep learning-based Super-Resolution system that aims to enhance the faces images captured from surveillance video in order to support suspect identification. The proposed system relies on an image processing technique called Super-Resolution that consists of recovering high-resolution images from low-resolution ones. More specifically, we used the Very-Deep Super-Resolution (VDSR) neural network to enhance the image quality. The proposed model was trained with CelebA faces dataset and used to enhance the resolution of the QMUL-SurvFace dataset. It yielded a Peak Signal-to-Noise Ratio (PSNR) improvement of 7% and Structural Similarity Index (SSIM) improvement of 3%. Most importantly, it increased the face recognition rate by 45.7%.
Highlights
Nowadays, video surveillance cameras are crucial for ensuring people's safety and security
We improve the performance of the LR faces images captured from surveillance recorded videos by adopting a Very-Deep Super-Resolution (VDSR) convolutional neural network-based system [8]
This experiment is conducted on the QMUL-SurvFace benchmark dataset [24]
Summary
Video surveillance cameras are crucial for ensuring people's safety and security. They become an essential evidence for investigating security related cases. The identification of the persons recorded in the surveillance video becomes decisive in solving such cases. The involved person identification is not always possible due to the low-resolution (LR) stored video frames. The resolution that refers to the amount of details can be defined as the number of pixels per frame length unit [1]. The resolution increases with the number of pixels [2]. Surveillance cameras can record highquality videos, they have limited storage space. The size of the recorded video is decreased, which leads to quality degradation. There are more factors that may affect the image quality like bad weather or the lightening conditions of the scene
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