Action recognition from extremely low-resolution thermal image sequence

Kawashima, Takayuki; Kawanishi, Yasutomo; Ide, Ichiro; Murase, Hiroshi; Deguchi, Daisuke; Aizawa, Tatsuhiko; Kawade, Masato

doi:10.1109/avss.2017.8078497

Cited by 38 publications

(19 citation statements)

References 13 publications

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“…In the field of ML, different researchers have used CNN based image enhancement techniques for the purpose of enhancing the resolution of the images [225]- [227]. Peng et al used deep CNN based approach, which categorizes the objects in images having low resolution [225].…”

Section: Cnns For Low Resolution Imagesmentioning

confidence: 99%

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A survey of the recent architectures of deep convolutional neural networks

et al. 2020

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Deep Convolutional Neural Networks (CNNs) are a special type of Neural Networks, which have shown exemplary performance on several competitions related to Computer Vision and Image Processing. Interesting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, Speech Recognition, etc. The powerful learning ability of deep CNN is largely due to the use of multiple feature extraction stages that can automatically learn representations from the data. Availability of a large amount of data and improvements in the hardware technology have accelerated the research in CNNs, and recently very interesting deep CNN architectures have been reported. In fact, several interesting ideas to bring advancements in CNNs have been explored such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the major improvement in representational capacity of the deep CNN is achieved through architectural innovations. Especially, the idea of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing has gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges and applications of CNN are also provided. CNNs are the best among learning algorithms in understanding images content, and have shown exemplary results in segmentation, classification, detection, and retrieval related tasks [8], [9]. The success of CNNs has captured attention beyond academia. In industry, companies such as Google, Microsoft, AT&T, NEC, and Facebook have developed active research groups for exploring new architectures of CNN [10]. At present, most of the frontrunners of image processing and computer vision competitions are employing deep CNN based models.The attractive feature of CNN is its ability to exploit spatial or time correlation of the data. The topology of CNN is divided into multiple learning stages composed of a combination of the convolutional layers, non-linear processing units, and subsampling layers [11]. CNNs are feedforward multilayered hierarchical networks that are similar to fully connected neural network where each layer, using a bank of convolutional kernels, performs multiple transformations [12]. Convolution operation extracts useful features from locally correlated data points. Output of the convolutional kernels is assigned to non-linear processing unit (activation function), which not only helps in learning abstractions but also emb...

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Section: Cnns For Low Resolution Imagesmentioning

confidence: 99%

“…Another, deep learning based technique is reported by Kawashima et al, in which convolutional layers along with a layer of LSTM is used to recognize action from thermal images of low resolution [227]. Network approach [238], [239].…”

Section: Cnns For Low Resolution Imagesmentioning

confidence: 99%

A survey of the recent architectures of deep convolutional neural networks

et al. 2020

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“…Traditional hand-crafted feature-based methods [273], [274] exploiting infrared data have been used for gait recognition [273], [275] and night HAR [274], [276]. Recently, plenty of deep learning architectures [277], [278] have also been proposed for infrared data-based HAR. In [277], a framework using convolutional layers for modeling spatial information and LSTM layers for modeling temporal dependencies was designed for HAR.…”

Section: Infrared Sequence Modalitymentioning

confidence: 99%

Human Action Recognition from Various Data Modalities: A Review

Rahmani¹,

Bennamoun²,

Ke³

2021

Preprint

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“…Kawashima [107] proposed a Deep Learning-based approach for action recognition method with an extremely low-resolution thermal image sequence. The hardware used is a grid of 16x16 far-infrared sensor array (Thermal sensor D6T-1616L by OMRON Corp.) mounted on the ceiling (around 220 cm above the floor) of a room.…”

Section: ) Thermal Imagingmentioning

confidence: 99%

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

et al. 2020

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Sensors are devices that quantify the physical aspects of the world around us. This ability is important to gain knowledge about human activities. Human Activity recognition plays an import role in people's everyday life. In order to solve many human-centered problems, such as health care, and individual assistance, the need to infer various simple to complex human activities is prominent. Therefore, having a well defined categorization of sensing technology is essential for the systematic design of human activity recognition systems. By extending the sensor categorization proposed by White, we survey the most prominent research works that utilize different sensing technologies for human activity recognition tasks. To the best of our knowledge, there is no thorough sensor-driven survey that considers all sensor categories in the domain of human activity recognition with respect to the sampled physical properties, including a detailed comparison across sensor categories. Thus, our contribution is to close this gap by providing an insight into the state-of-the-art developments. We identify the limitations with respect to the hardware and software characteristics of each sensor category and draw comparisons based on benchmark features retrieved from the research works introduced in this survey. Finally, we conclude with general remarks and provide future research directions for human activity recognition within the presented sensor categorization. INDEX TERMS Sensor categorization, human activity recognition, public databases for human activity recognition, physical sensors, sensor benchmark.

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Action recognition from extremely low-resolution thermal image sequence

Cited by 38 publications

References 13 publications

A survey of the recent architectures of deep convolutional neural networks

A survey of the recent architectures of deep convolutional neural networks

Human Action Recognition from Various Data Modalities: A Review

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

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