Deep learning based missing object detection and person identification: an application for smart CCTV

Abstract

Security and protection are the most crucial concerns in today’s quickly developing world. Deep Learning methods and computer vision assist in resolving both problems. One of the computer vision subtasks that allows us to recognise things is object detection. Videos are a source that is taken into account for detection, and image processing technology helps to increase the effectiveness of state-ofthe-art techniques. With all of these technologies, CCTV is recognised as a key element. Using a deep convolutional neural network, we accept CCTV data in real time in this article. The main objective is to make content the centre of things. Using the YOLO technique, we were able to detect the missing item with an improvement of 10% sparsity over the current state-of-the-art algorithm in the context of surveillance systems, where object detection is a crucial step. It can be utilised to take immediate additional action.

A Swarming Meyer Wavelet Computing Approach to Solve the Transport System of Goods

“…The designed structure can be tested in the future for solving quantum models [37], lonngren-wave systems [38], singular models [39], nonlinear differential models [40], fractional types of systems [41][42][43][44], pricing economy networks [45], Gemini virus models [46], and other related systems [47][48][49][50].…”

Integrated Stochastic Investigation of Singularly Perturbed Delay Differential Equations for the Neuronal Variability Model

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Application of mathematical models for the optimization and improvement of related logistics processes: reduction of carbón footprint and improvement of efficient driving

“…The designed structure can be tested in the future for solving quantum models [37], lonngren-wave systems [38], singular models [39], nonlinear differential models [40], fractional types of systems [41][42][43][44], pricing economy networks [45], Gemini virus models [46], and other related systems [47][48][49][50]. -73 -…”