Implementation of Template Matching Method for Door Lock Security System Using Raspberry Pi

Widiawan, Beni; Kautsar, Syamsiar; Purnomo, Fendik Eko; Etikasari, Bety

doi:10.30870/volt.v2i2.2047

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Applications of template matching for image-based pattern recognition can be widely found in the literature. One example of such implementation is a door lock security system using Raspberry Pi proposed in [10]. This study presented a system based on image processing to replace the use of RFID that gives less flexibility.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Template Matching on Detection of Stop Line Violations

Larasati

Setyawan

2021

JNTE

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Road marking is a sign located above the road surface that combines either a line or a symbol and provides information as visual guidance to road users. One example of road marking is the stop line behind the zebra crossing. Many violations of pedestrian rights are committed by motorized vehicles, especially because vehicles do not stop behind the stop line. This paper proposes a system to detect this type of traffic violation using the template matching method. This method is a technique in digital image processing to find small parts of the image that match the image template, so it can detect whether there is a traffic violation or not. This system uses the appropriate template images for each dataset (morning, afternoon, and evening). The system produces accuracy performances for the morning and afternoon dataset of about 100% and 78% respectively, and for the evening dataset is 70%. So, the overall average accuracy rate is 83%. The main factor affecting the overall accuracy performance is the inability to automatically extract the best template for the dataset from the morning and evening datasets.

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Section: Introductionmentioning

confidence: 99%

Implementation of Template Matching on Detection of Stop Line Violations

Larasati

Setyawan

2021

JNTE

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On-line monitoring system in greenhouse area for chrysanthemum cultivation based on raspberry pi and iot

Riskiawan

Anwar

Kautsar

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

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Chrysanthemum plants generally grow well when cultivated at an altitude of 650-1200 m above sea level. Chrysanthemum plants come from subtropical climates, with temperatures ranging from 17-30 C. whereas, in Indonesia, which is a tropical region, it is 20-26 C. The humidity needed by chrysanthemum plants at the time of planting is 90-95%, while at the time of enlargement and chrysanthemum flower growth of 70-80%. To get optimal results, chrysanthemum plants need to be cultivated in a greenhouse area. In a greenhouse, temperature and humidity can be controlled. In addition, chrysanthemum plants are also more protected from pests. In this paper, an on-line greenhouse monitoring system is developed. The system is equipped with sensors for temperature, humidity, soil humidity, light intensity, and an RGB camera. Sensor data is sent to the cloud database via raspberry pi. Data is displayed in graphical form every 1 hour. The sensor data can be accessed from anywhere via the internet network and can be used for the analysis of crop yields based on greenhouse conditions during cultivation.

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Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing

Greco,

Fasihiany,

Ranjbar

et al. 2024

Algorithms

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The primary objective of a depalletizing system is to automate the process of detecting and locating specific variable-shaped objects on a pallet, allowing a robotic system to accurately unstack them. Although many solutions exist for the problem in industrial and manufacturing settings, the application to small-scale scenarios such as retail vending machines and small warehouses has not received much attention so far. This paper presents a comparative analysis of four different computer vision algorithms for the depalletizing task, implemented on a Raspberry Pi 4, a very popular single-board computer with low computer power suitable for the IoT and edge computing. The algorithms evaluated include the following: pattern matching, scale-invariant feature transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifier. Each technique is described and their implementations are outlined. Their evaluation is performed on the task of box detection and localization in the test images to assess their suitability in a depalletizing system. The performance of the algorithms is given in terms of accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The results reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of a depalletizing system.

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Implementation of Template Matching Method for Door Lock Security System Using Raspberry Pi

Cited by 3 publications

References 4 publications

Implementation of Template Matching on Detection of Stop Line Violations

Implementation of Template Matching on Detection of Stop Line Violations

On-line monitoring system in greenhouse area for chrysanthemum cultivation based on raspberry pi and iot

Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing

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