Action detection and classification in kitchen activities videos using graph decoding

Ramadan, Mona; El-Jaroudi, A.

doi:10.1007/s00371-021-02346-5

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Finally, in [39][40][41], cooking activities using ML and DL techniques are classified. These models are useful for recognizing situations such as cutting, baking, washing, or cooking.…”

Section: Discussionmentioning

confidence: 99%

Cooktop Sensing Based on a YOLO Object Detection Algorithm

Azurmendi

Zulueta

López-Guede

et al. 2023

Sensors

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Deep Learning (DL) has provided a significant breakthrough in many areas of research and industry. The development of Convolutional Neural Networks (CNNs) has enabled the improvement of computer vision-based techniques, making the information gathered from cameras more useful. For this reason, recently, studies have been carried out on the use of image-based DL in some areas of people’s daily life. In this paper, an object detection-based algorithm is proposed to modify and improve the user experience in relation to the use of cooking appliances. The algorithm can sense common kitchen objects and identify interesting situations for users. Some of these situations are the detection of utensils on lit hobs, recognition of boiling, smoking and oil in kitchenware, and determination of good cookware size adjustment, among others. In addition, the authors have achieved sensor fusion by using a cooker hob with Bluetooth connectivity, so it is possible to automatically interact with it via an external device such as a computer or a mobile phone. Our main contribution focuses on supporting people when they are cooking, controlling heaters, or alerting them with different types of alarms. To the best of our knowledge, this is the first time a YOLO algorithm has been used to control the cooktop by means of visual sensorization. Moreover, this research paper provides a comparison of the detection performance among different YOLO networks. Additionally, a dataset of more than 7500 images has been generated and multiple data augmentation techniques have been compared. The results show that YOLOv5s can successfully detect common kitchen objects with high accuracy and fast speed, and it can be employed for realistic cooking environment applications. Finally, multiple examples of the identification of interesting situations and how we act on the cooktop are presented.

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“…Finally, in [39][40][41], cooking activities using ML and DL techniques are classified. These models are useful for recognizing situations such as cutting, baking, washing, or cooking.…”

Section: Discussionmentioning

confidence: 99%

Cooktop Sensing Based on a YOLO Object Detection Algorithm

Azurmendi

Zulueta

López-Guede

et al. 2023

Sensors

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A Kitchen Standard Dress Detection Method Based on the YOLOv5s Embedded Model

Zhou¹,

Zhou

Pan

et al. 2023

Applied Sciences

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In order to quickly and accurately detect whether a chef is wearing a hat and mask, a kitchen standard dress detection method based on the YOLOv5s embedded model is proposed. Firstly, a complete kitchen scene dataset was constructed, and the introduction of images for the wearing of masks and hats allows for the low reliability problem caused by a single detection object to be effectively avoided. Secondly, the embedded detection system based on Jetson Xavier NX was introduced into kitchen standard dress detection for the first time, which accurately realizes real-time detection and early warning of non-standard dress. Among them, the combination of YOLOv5 and DeepStream SDK effectively improved the accuracy and effectiveness of standard dress detection in the complex kitchen background. Multiple sets of experiments show that the detection system based on YOLOv5s has the highest average accuracy of 0.857 and the fastest speed of 31.42 FPS. Therefore, the proposed detection method provided strong technical support for kitchen hygiene and food safety.

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Action detection and classification in kitchen activities videos using graph decoding

Cited by 2 publications

References 32 publications

Cooktop Sensing Based on a YOLO Object Detection Algorithm

Cooktop Sensing Based on a YOLO Object Detection Algorithm

A Kitchen Standard Dress Detection Method Based on the YOLOv5s Embedded Model

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