Human detection techniques for real time surveillance: a comprehensive survey
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Human detection techniques for real time surveillance: a comprehensive survey Mohd. Aquib Ansari1
· Dushyant Kumar Singh1
Received: 10 April 2020 / Revised: 12 September 2020 / Accepted: 19 October 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Real-time detection of humans is an evolutionary research topic. It is an essential and prominent component of various vision based applications. Detection of humans in real-time video sequences is an arduous and challenging task due to various constraints like cluttered environment, occlusion, noise, etc. Many researchers are doing their research in this area and have published the number of researches so far. Determining humans in visual monitoring system is prominent for different types of applications like person detection and identification, fall detection for an elder person, abnormal surveillance, gender classification, crowd analysis, person gait characterization, etc. The main objective of this paper is to provide a comprehensive survey of the various challenges and modern developments seen for human detection methodologies in day vision. This paper consists of an overview of different human detection techniques and their classification based on various underlying factors. The algorithmic technicalities with their applicability to these techniques are deliberated in detail in the manuscript. Different humanitarian imperative factors have also been highlighted for comparative analysis of each human detection methodology. Our survey shows the difference between current research and future requirements. Keywords Human detection · Feature description · Deep convolutional neural networks · Recent progress · Surveillance · Computer vision
1 Introduction In recent years, video footages are attracting more attention due to its elaborate applications for humans to detect. The footages that are acquired from the cameras for surveillance are generally accompanied by lower resolution. The vast majority of the scenes caught by a static camera are accompanied with negligible variations in the background. These footages are used to detect, track and analysis of individual behavior for the surveillance. Surveillance is a very crucial topic in computer vision. It can be used in various areas for security purposes like pedestrian detection, driver assistance systems, gender recognition, person Mohd. Aquib Ansari
[email protected] 1
CSED, MNNIT Allahabad, Prayagraj, UP, India
Multimedia Tools and Applications
counting in the dense crowd, etc. These days, the surveillance system is also acting as a crime deterrent that helps to discourage criminals from carrying out illegal activities. Most existing video surveillance frameworks depend on human spectators for identifying particular events in the real-time video sequences. But, there are some impediments in the human ability to observe the concurrent proceedings in the surveillance displays. Therefore, the automatic video surveillance system [7, 22, 28, 50, 113, 127, 152, 168, 189] is n
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