Application of foreground object patterns analysis for event detection in an innovative video surveillance system

Frejlichowski, Dariusz; Gościewska, Katarzyna; Forczmański, Paweł; Hofman, Radosław

doi:10.1007/s10044-014-0405-7

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…Convolutional and pool layers are stacked one after the other. In contrast, layers with fully connected neurons generate probabilities of class membership [17,18]. e structure was chosen experimentally.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning for Plastic Waste Classification System

Bobulski

Kubanek

2021

Applied Computational Intelligence and Soft Computing

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Plastic waste management is a challenge for the whole world. Manual sorting of garbage is a difficult and expensive process, which is why scientists create and study automated sorting methods that increase the efficiency of the recycling process. The plastic waste may be automatically chosen on a transmission belt for waste removal by using methods of image processing and artificial intelligence, especially deep learning, to improve the recycling process. Waste segregation techniques and procedures are applied to major groups of materials such as paper, plastic, metal, and glass. Though, the biggest challenge is separating different materials types in a group, for example, sorting different colours of glass or plastics types. The issue of plastic garbage is important due to the possibility of recycling only certain types of plastic (PET can be converted into polyester material). Therefore, we should look for ways to separate this waste. One of the opportunities is the use of deep learning and convolutional neural network. In household waste, the most problematic are plastic components, and the main types are polyethylene, polypropylene, and polystyrene. The main problem considered in this article is creating an automatic plastic waste segregation method, which can separate garbage into four mentioned categories, PS, PP, PE-HD, and PET, and could be applicable on a sorting plant or home by citizens. We proposed a technique that can apply in portable devices for waste recognizing which would be helpful in solving urban waste problems.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning for Plastic Waste Classification System

Bobulski

Kubanek

2021

Applied Computational Intelligence and Soft Computing

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“…Video surveillance system [14,15] can transport the home video stream to Android devices, which makes users monitor home situation in real time. In this paper, the robot is installed with the webcam based on HTTP and TCP, which can encapsulate video stream data into HTTP data groups and provide the web access function, and then the client uses the TCP to get the video stream.…”

Section: B Video Surveillance Systemmentioning

confidence: 99%

The Remote Control System of Domestic Robots Based on Android

Lin¹,

Jiang²,

Jiang³

2018

Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)

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Abstract-Based on self-developed and somatosensorymapping robot, a remote control system of domestic robots is designed in this paper, in which the system adopts intelligent speech recognition technology in terms of C/S framework such that the speech control robot has been used to do some basic housework. Combining with HTTP and MQTT, the robot can be employed to do the video surveillance and environmental monitoring. By delivering video stream and environmental parameters to Android devices, users can remotely monitor house situation easily, which makes the control of domestic robots smarter and easier.

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