Comprehensive Analysis of Video Surveillance System and Applications

In this growing age of technology, various sensors are used to capture data from their nearby environments. The captured data is multimedia in nature. For example, CCTV cameras are used in those places where security matters or where continuous monitoring is required. Hence object detection, object recognition, and face recognition became key elements of city surveillance applications. Manual surveillance seems time-consuming and requires huge space to store the data; hence video surveillance has a significant contribution to unstructured big data. All surveillance techniques and approaches are based on Object Tracking, Target Tracking, Object Recognition, and Object Mobile Tracking Systems (OMTS). The main difficulty, however, lies in effectively processing them in real time. Therefore, finding a solution still needs careful consideration. This paper mainly targeting to the smart city surveillance system and inspects all existing surveillance systems based on various tremendous technologies like a wireless sensor network, machine learning, and Deep Learning. The author discovered the problems in the existing methods and summarized them in the paper. The motive is to point out the various challenges and offer new research prospects for the multimedia-oriented surveillance system over the traditional surveillance system for the smart city network architecture. The thorough survey in this paper starts with object recognition and goes toward action recognition, image annotation, and scene understanding. This comprehensive survey summarizes the comparative analysis of algorithms, models, and datasets in addition to targeting the methodologies. 

Keywords: Deep learning, Face-recognition , Image annotation, Multimedia, Machine learning, OMTS, Object detection, Smart city, Video surveillance.