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Acceleration of Computer Vision and Deep Learning: Surveillance Systems
Abstract
Cameras used for surveillance have grown in popularity since the technology boom and are now part of our everyday life. It appears to be laborious and time-consuming to monitor the surveillance cameras manually. Computer vision is reshaping the security and surveillance industry. Despite their revolutionary nature, modern CCTV cameras are insufficient because they are passive units that assist investigations but do not give preventative measures. They need to be replaced with active systems. Recreational issues in public security necessitate computer vision, artificial intelligence, and machine learning (ML). Due to the advancements in deep surveillance, we should expect a massive boost in inefficiency. The outstanding performance in image identification and the capacity to absorb temporal information that convolutional and recurrent neural networks offer intelligent surveillance systems bodes well for their future growth in this area. The goal of this chapter is to examine the challenges in surveillance systems, the usage of deep learning techniques, and various new applications.
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