Development methods for hybrid motion detection (frame difference-automatic threshold)

Wirayuda, Tjokorda Agung Budi; Laksitowening, Kusuma Ayu; Sthevanie, Febryanti; Rismala, Rita

doi:10.1109/icoict.2013.6574577

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Various studies on motion detection have been widely practiced, among others, Metode Subtraction (Wu et al, 2017) and Algoritma Gaussian Mixture Model (Pratama et al, 2017;Ji and Yu, 2013), Sobel Algoritma (Rahman, 2017;Zhang et al, 2016), Optimal Motion Detection Algorithm (Antonius et al, 2015), Frame Differences (Zul et al, 2015;Wirayuda et al, 2013) and Illumination of Change Detection (Priadana et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Background Subtraction, Sobel, Adaptive Motion Detection, Frame Differences, and Accumulative Differences Images on Motion Detection

Ramadhan

Sari

2018

Sinergi

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Nowadays, digital image processing is not only used to recognize motionless objects, but also used to recognize motions objects on video. One use of moving object recognition on video is to detect motion, which implementation can be used on security cameras. Various methods used to detect motion have been developed so that in this research compared some motion detection methods, namely Background Substraction, Adaptive Motion Detection, Sobel, Frame Differences and Accumulative Differences Images (ADI). Each method has a different level of accuracy. In the background substraction method, the result obtained 86.1% accuracy in the room and 88.3% outdoors. In the sobel method the result of motion detection depends on the lighting conditions of the room being supervised. When the room is in bright condition, the accuracy of the system decreases and when the room is dark, the accuracy of the system increases with an accuracy of 80%. In the adaptive motion detection method, motion can be detected with a condition in camera visibility there is no object that is easy to move. In the frame difference method, testing on RBG image using average computation with threshold of 35 gives the best value. In the ADI method, the result of accuracy in motion detection reached 95.12%.

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Section: Introductionmentioning

confidence: 99%

Comparison of Background Subtraction, Sobel, Adaptive Motion Detection, Frame Differences, and Accumulative Differences Images on Motion Detection

Ramadhan

Sari

2018

Sinergi

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“…The installation uses a camera as a sensor device, and a simple motion detection algorithm (Wirayuda et al, 2013) to characterize the audience's movements. The musical generation, based on Markov chains (Schulze & Van der Merwe, 2011;Pachet, 2002;Cope, 1997), aims at converting the detected movement intensity into the intensity of the musical manifestation without requiring previous musical knowledge from the audience.…”

Section: Introductionmentioning

confidence: 99%

An interactive audio-visual installation using ubiquitous hardware and web-based software deployment

Tavares

2015

PeerJ Computer Science

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This paper describes an interactive audio-visual musical installation, namely MOTUS, that aims at being deployed using low-cost hardware and software. This was achieved by writing the software as a web application and using only hardware pieces that are built-in most modern personal computers. This scenario implies in specific technical restrictions, which leads to solutions combining both technical and artistic aspects of the installation. The resulting system is versatile and can be freely used from any computer with Internet access. Spontaneous feedback from the audience has shown that the provided experience is interesting and engaging, regardless of the use of minimal hardware.

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“…Furthermore, it is also can be used to detect moving objects. Simplicity of this process that might result in short processing time is the advantage of this method [10]. The following figure shows an image or frame in a video with smoke along with the marker of the image showing the background marked in black and the foreground marked in white.…”

Section: B Frame Differencingmentioning

confidence: 99%

“…A pixel will be considered as smoke if: (10) Where the values of the thresholds for Saturation and Value were experimentally determined using number of training videos. Then each pixels in the block that considered as smoke are summed up.…”

Section: B Color Analysismentioning

confidence: 99%

Early Smoke Detection on Video Using Wavelet Energy

Shafar

Wirayuda

Sthevanie

2017

IndoJC

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Most of the smoke detection system these days still using sensors that have to receive specific particles before it could give a warning. But, this system takes some time to react and quite difficult to place in spacious room or the outdoor. To overcome this, there is some research that build smoke detection system using many kind video processing technique that could provide early warning. In this research, wavelet energy was used to detect smoke in the video. To determine candidate blocks in a frame that contain smoke, this research performed background subtraction and color analysis based on HSV color space. Then implementing spatial analysis and spatio-temporal analysis by using wavelet energy method and accumulative motion orientation to detect the smoke. This system using combination of dataset from previous research [1], downloaded from various sources and selfmade dataset. Based on testing process using those dataset, this system reaches 91.05% accuracy for block-level and 72.22% accuracy for frame-level.Keywords: Accumulative motion orientation, smoke detection, spatial analysis, spatio-temporal analysis, video processing, wavelet energy Abstrak Sebagian besar sistem deteksi asap saat ini masih menggunakan sensor yang akan menangkap partikel tertentu sebelum dapat memberi peringatan. Namun, sistem ini membutuhkan waktu yang cukup lama untuk bereaksi dan sulit jika dipasang di ruang luas ataupun terbuka. Untuk mengatasi kekurangan ini, banyak dilakukan penelitian yang membangun sistem deteksi asap menggunakan berbagai teknik video processing yang dapat lebih menjamin untuk memberi peringatan dini. Dalam penelitian ini, digunakan metode wavelet energy untuk mendeteksi asap pada video. Untuk menentukan kandidat blok pada frame yang mengandung asap, dilakukan background subtraction dan analisa warna berdasarkan model HSV. Lalu digunakan metode wavelet energy pada spatial analysis dan spatio-temporal analysis serta metode accumulative motion orientation untuk mendeteksi asap. Dataset yang digunakan pada sistem yang dibangun merupakan kombinasi dari dataset penelitian sebelumnya [1], unduhan dari berbagai sumber serta dataet yang dibangun secara mandiri. Berdasarkan pengujian yang dilakukan menggunakan dataset tersebut, sistem ini menghasilkan tingkat akurasi sebesar pada level blok sebesar 91.05% dan tingkat akurasi pada level frame sebesar 72.22%.

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Development methods for hybrid motion detection (frame difference-automatic threshold)

Cited by 7 publications

References 8 publications

Comparison of Background Subtraction, Sobel, Adaptive Motion Detection, Frame Differences, and Accumulative Differences Images on Motion Detection

Comparison of Background Subtraction, Sobel, Adaptive Motion Detection, Frame Differences, and Accumulative Differences Images on Motion Detection

An interactive audio-visual installation using ubiquitous hardware and web-based software deployment

Early Smoke Detection on Video Using Wavelet Energy

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