Human action recognition for night vision using temporal templates with infrared thermal camera

Eum, Hyukmin; Lee, Jeisung; Yoon, Changwoo; Park, Mignon

doi:10.1109/urai.2013.6677407

Cited by 21 publications

(17 citation statements)

References 8 publications

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“…Our method is mainly focused on behavioral recognition based on frontal-viewing human body, like most previous studies [ 17 , 18 , 19 , 20 , 26 , 27 , 30 , 32 , 33 , 34 , 35 , 36 , 38 , 39 , 41 , 42 , 51 ]. The change of view direction (from frontal view to side view) does not affect the recognition accuracy of “standing” and “sitting” of Figure 3 .…”

Section: Resultsmentioning

confidence: 99%

“…However, the process of obtaining optical flow is computationally expensive, and it is also sensitive to noise and illumination changes [ 27 , 28 , 32 ]. Various groups [ 32 , 33 , 34 , 35 , 36 , 37 ] have performed behavior recognition based on a gait flow image (GFI), gait energy image (GEI), gait history image (GHI), accumulated motion image (AMI), and motion history image (MHI). These studies focused on obtaining a rough foreground region in which motion occurs, but the attempt to determine the exact path of motion of hands and legs was less satisfactory.…”

Section: Introductionmentioning

confidence: 99%

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Robust Behavior Recognition in Intelligent Surveillance Environments

Batchuluun

Kim

et al. 2016

Sensors

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Behavior Recognition in Intelligent Surveillance Environments

Batchuluun

Kim

et al. 2016

Sensors

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show abstract

“…However, these contour-based methods are not methods for removing halo effects in images, but rather, approaches for accurately detecting subjects in thermal images with halo effects. Moreover, other existing studies that have investigated the detection [19]- [26], identification [27], [28], and recognition [29]- [31] of thermal images and the survey study [32] did not consider these two problems. Therefore, we propose an image transformation method based on the regions of thermal reflection using deep learning.…”

Section: Related Workmentioning

confidence: 99%

Region-Based Removal of Thermal Reflection Using Pruned Fully Convolutional Network

et al. 2020

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In general, an image obtained from a thermal camera often has a mirror reflection or shadow reflected off the ground around an object, which is referred to as thermal reflection. Sometimes the thermal reflections are connected to their objects in images, which makes it difficult to detect or recognize the object only. Thermal reflections sometimes occur on the wall near an object and are detected as another object when they are not connected to the object. Furthermore, the size of thermal reflection and pixel value significantly vary with the medium of the reflected range and the surrounding temperature. In these cases, the patterns and pixel values of thermal reflection and the object become similar and difficult to distinguish. However, there are insufficient studies on removing the thermal reflection of various kinds of objects in diverse environments. Therefore, in this paper, we propose a pruned fully convolutional network (PFCN)-based method for removing the thermal reflection of an object using the surrounding information when image transformation is performed only within the region of an object. When experiments were conducted using self-collected databases (Dongguk thermal image database (DTh-DB) and Dongguk items & vehicles database (DI&V-DB)) and open databases, the method proposed herein exhibited more outstanding performance in removing thermal reflection when compared with the state-of-the-art methods. INDEX TERMS Thermal image, image transform, thermal reflection removal, pruned fully convolutional network.

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“…Eum et al [4] focuses on the problem of detecting human action at night. The paper aims at using infrared thermal camera for video surveillance.…”

Section: Human Action Recognition For Night Vision Using Temporal Temmentioning

confidence: 99%

Automated Human Detection and Tracking for Surveillance Applications

Tripathi¹,

Pathak²,

Sharma³

et al. 2019

IJIVP

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Safeguarding a country's border is very vital to its nation. A nation's security is determined by the kind of security imposed at the border. Proper surveillance system needs to be imposed at border because of numerous reasons which include drug smuggling, illegal immigrants crossing the borders and last but not the least, terrorist intrusion. To protect our border from these activities we need to monitor, filter and detect if there is any motion or any kind of activity that might lead to intrusion in our borders. For this we need 247, surveillance and hence an automated surveillance system needs to be developed. The existing systems are not efficient enough to detect threats if the intruders are using some kind of camouflage or cover. To overcome these drawbacks of the existing systems we intend to use thermal imaging cameras. The focus is to use thermal imaging cameras to capture the images for detection and tracking of the human/object, so as to analyze and predict their behaviour and intentions. The use of thermal imaging unlike the optical camera will help the system to perform detection of suspects which are hidden behind an obstacle for e.g. a bush. The thermal imaging will perform the detection at night with the same efficiency as in the daytime. The surveillance system is designed in order to achieve the objective of safeguarding the border, thus satisfying the aim of detecting threats and minimizing false alarms.

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Human action recognition for night vision using temporal templates with infrared thermal camera

Cited by 21 publications

References 8 publications

Robust Behavior Recognition in Intelligent Surveillance Environments

Robust Behavior Recognition in Intelligent Surveillance Environments

Region-Based Removal of Thermal Reflection Using Pruned Fully Convolutional Network

Automated Human Detection and Tracking for Surveillance Applications

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