Abstract
Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.
Highlights
1.1 Introduction Over the recent years, detecting human beings in a video scene of a surveillance system is attracting more attention due to its wide range of applications in abnormal event detection, human gait characterization, person counting in a dense crowd, person identification, gender classification, fall detection for elderly people, etc
The key purpose of this paper is to provide a comprehensive review on studies conducted in the area of human detection process of a visual surveillance system
New pixel values update the mixture of Gaussian (MoG) using an online K-means approximation
Summary
Over the recent years, detecting human beings in a video scene of a surveillance system is attracting more attention due to its wide range of applications in abnormal event detection, human gait characterization, person counting in a dense crowd, person identification, gender classification, fall detection for elderly people, etc.The scenes obtained from a surveillance video are usually with low resolution. Background subtraction is a popular method to detect an object as a foreground by segmenting it from a scene of a surveillance camera. A good background model needs to be adaptive to the changes in dynamic scenes. Stauffer and Grimson [2] introduced an adaptive Gaussian mixture model, which is sensitive to the changes in dynamic scenes derived from illumination changes, extraneous events, etc. In [13], a non-parametric model is proposed for background modelling, where a kernel-based function is employed to represent the colour distribution of each background pixel. NP performs very well compared to MoG-based algorithm. Temporal differencing [19,20,47,69] High Low to moderate
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