Abstract
This article discusses the approach of human detection and tracking in a homogeneous domain using surveillance cameras. This is a vast area in which significant research has been taking place from more than a decade and the paper is about detection of a human and its face in a given video and stores Local Binary Pattern Histogram (LBPH) features of the detected faces. Once a human is detected in the video, that person will be given a label and him/her is tracked in different video taken by multiple cameras by the application of machine learning and image processing with the help of OpenCV. Many algorithms were used for detecting, recognizing and tracking till date, thus in this paper, main thing is the comparison of the proposed algorithm with some among the state-of-the-art algorithms. And also shows how the proposed algorithm is better than the other chosen algorithms.
Highlights
The observation or monitoring of activity, behavior and other information by a system which includes several Closed Circuit Television (CCTV) cameras for observation and a set of algorithms to track a person is called Surveillance system
Once the security cameras came into existence, it became easy to find people passing within the range of CCTV camera by searching through the videos recorded
Inventions increase people’s expectations, security camera reduces the human effort one has to search for an individual through the entire video which takes a considerable amount of time
Summary
The observation or monitoring of activity, behavior and other information by a system which includes several Closed Circuit Television (CCTV) cameras for observation and a set of algorithms to track a person is called Surveillance system. Detecting a face from a video and tracking a person in that video is a very challenging task as there will be several changes including the resolution when a surveillance camera is used, and the processing and computation of the frames taken from a surveillance camera in a real-time environment will be a critical and very challenging task because of many reasons. Apart from these, there are some more serious problems that need to be taken into consideration are: 1) Features: Features of the person (spectacles, eyes, facial features, different hairstyles, body changes, facial hair) matter a lot while he/she is being tracked. 7) Twins/Triplets: It is a bit tough for humans only to differentiate between twins/triplets These are some of the serious problems that are needed to be taken into consideration while implementing the algorithms [6]. Tensorflow can be used in heterogeneous and large scale environments; it is mainly an advantage when it comes to the performance of an algorithm [11]
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