Robust Behavior Recognition in Intelligent Surveillance Environments

Abstract

Intelligent surveillance systems have been studied by many researchers. These systems should be operated in both daytime and nighttime, but objects are invisible in images captured by visible light camera during the night. Therefore, near infrared (NIR) cameras, thermal cameras (based on medium-wavelength infrared (MWIR), and long-wavelength infrared (LWIR) light) have been considered for usage during the nighttime as an alternative. Due to the usage during both daytime and nighttime, and the limitation of requiring an additional NIR illuminator (which should illuminate a wide area over a great distance) for NIR cameras during the nighttime, a dual system of visible light and thermal cameras is used in our research, and we propose a new behavior recognition in intelligent surveillance environments. Twelve datasets were compiled by collecting data in various environments, and they were used to obtain experimental results. The recognition accuracy of our method was found to be 97.6%, thereby confirming the ability of our method to outperform previous methods.

Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification

“…We then build two functions of θ(x) and g(x) by a 1×1 convolution of batch normalization layer and ReLU activation layer to obtain feature maps with shape hw × c/r, where r is a reduction factor of the feature channel. The covariance matrix is then calculated with θ(x), as following in formula (5)…”

“…Compared with face recognition, the person re-identification scene is closer to the real environment, but it is more difficult and challenging to achieve under the dramatic variations with respect to illumination, occlusion, resolution, human pose, view angle, clothing, and background. In previous studies [3]- [5], the authors use gait and movement behaviors as dynamic features for person identification. These methods do not require high image resolution, so they can identify a person at a greater distance.…”

Low Cost Efficient Deliverying Video Surveillance Service to Moving Guard for Smart Home

“…We are not interested in the segmentation of objects or the affectation of environmental conditions, but in a real-time reception of images that facilitates a first vision on the part of the guard who observes the intrusion in the Smart Home. In [ 25 ] is treated the problem of intelligent recognition of objects in nigthtime using visible light cameras. They proposed an interesting image recognition system for near infrared cameras that can operate in daytime and nighttime.…”

A Discriminative Person Re-Identification Model With Global-Local Attention and Adaptive Weighted Rank List Loss

“…In [4,5], researchers use gait features and body movements to identify specific people. In order to enable the monitor to be used in the dark environment, the authors [50][51][52] use thermal imaging technology to identify human behavior. Compared with gait recognition and behavior recognition, the person re-identification only needs to extract the appearance features of the person, such as clothes, shoes, and bags.…”