Faster RCNN Detection Based OpenCV CSRT Tracker Using Drone Data

Farhodov, Xurshedjon; Kwon, Oh-Heum; Kang, Kyung Won; Lee, Suk-Hwan; Kwon, Ki-Ryong

doi:10.1109/icisct47635.2019.9012043

Cited by 20 publications

(9 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After that, the target is localized by the probability map, and its region is updated. Furthermore, Farhodov et al (2019) integrated the region-based CNN pretrained object detection model and the CSRT tracker and got better tracking results since the detection model has already separated the target and the background. Therefore, we integrate the U-Net model and the CSRT algorithm; i.e., the filaments detected by the U-Net model are used as inputs of the CSRT tracker.…”

Section: Tracking Methodsmentioning

confidence: 99%

Developing an Automated Detection, Tracking, and Analysis Method for Solar Filaments Observed by CHASE via Machine Learning

Zheng,

Hao,

Qiu

et al. 2024

ApJ

View full text Add to dashboard Cite

Studies on the dynamics of solar filaments have significant implications for understanding their formation, evolution, and eruption, which are of great importance for space weather warning and forecasting. The Hα Imaging Spectrograph (HIS) on board the recently launched Chinese Hα Solar Explorer (CHASE) can provide full-disk solar Hα spectroscopic observations, which bring us an opportunity to systematically explore and analyze the plasma dynamics of filaments. The dramatically increased observation data require automated processing and analysis, which are impossible if dealt with manually. In this paper, we utilize the U-Net model to identify filaments and implement the Channel and Spatial Reliability Tracking algorithm for automated filament tracking. In addition, we use the cloud model to invert the line-of-sight velocity of filaments and employ the graph theory algorithm to extract the filament spine, which can advance our understanding of the dynamics of filaments. The favorable test performance confirms the validity of our method, which will be implemented in the following statistical analyses of filament features and dynamics of CHASE/HIS observations.

show abstract

Section: Tracking Methodsmentioning

confidence: 99%

Developing an Automated Detection, Tracking, and Analysis Method for Solar Filaments Observed by CHASE via Machine Learning

Zheng,

Hao,

Qiu

et al. 2024

ApJ

View full text Add to dashboard Cite

show abstract

“…The synergy of cascade face detection and the CSRT tracker ensures the continuous tracking of human faces. When the subject stands with their back turned toward the camera, the CSRT tracker can still operate well according to the pixel and local information [ 30 , 31 ]. Figure 4 shows the tracking results of the synergy of cascade face detection and CSRT tracker.…”

Section: Methodsmentioning

confidence: 99%

“…To resolve this predicament, we propose a new framework for fall detection by hybridizing the unique sensing mechanisms of video and ultrasonic sensors. We adopt the technique of cascade face detection [ 27 , 28 , 29 ] to detect the potential tracked subject and a channel and spatial reliability tracking (CSRT) tracker [ 30 , 31 ] for continuous subject tracking. After confirming the tracked subject, the hybridized sensing module collects the longitudinal and transverse motional trajectories from the moving subject.…”

Section: Introductionmentioning

confidence: 99%

Smart Fall Detection Framework Using Hybridized Video and Ultrasonic Sensors

Hsu

Chang

et al. 2021

Micromachines

View full text Add to dashboard Cite

Fall accidents can cause severe impacts on the physical health and the quality of life of those who suffer limb diseases or injuries, the elderly, and their caregivers. Moreover, the later the accident is discovered, the lower the chance of recovery of the injured one. In order to detect accidents earlier, we propose a data-driven human fall detection framework. By combining the sensing mechanism of a commercialized webcam and an ultrasonic sensor array, we develop a probability model for automatic human fall monitoring. The webcam and ultrasonic array respectively collect the transverse and longitudinal time-series signals from a moving subject, and then these signals are assembled as a three-dimensional (3D) movement trajectory map. We also use two different detection-tracking algorithms for recognizing the tracked subjects. The mean height of the subjects is 164.2 ± 12 cm. Based on the data density functional theory (DDFT), we use the 3D motion data to estimate the cluster numbers and their cluster boundaries. We also employ the Gaussian mixture model as the DDFT kernel. Then, we utilize those features to build a probabilistic model of human falling. The model visually exhibits three possible states of human motions: normal motion, transition, and falling. The acceptable detection accuracy and the small model size reveals the feasibility of the proposed hybridized platform. The time from starting the alarm to an actual fall is on average about 0.7 s in our platform. The proposed sensing mechanisms offer 90% accuracy, 90% sensitivity, and 95% precision in the data validation. Then these vital results validate that the proposed framework has comparable performance to the contemporary methods.

show abstract

“…The AGV design was carried out using 3D SketchUp software [19]. The algorithm was designed and implemented using Python 3.9.1 [20], while the computer vision library OpenCV [21] was used for image processing commands. After that, the AGV speed control and steering control algorithms were implemented in Python [22], while the Raspberry Pi 4 model B viewer board was run on a laptop using VNC Viewer software [23].…”

Section: B Agv Software Setupmentioning

confidence: 99%

Pengenalan Tanda Arah untuk Navigasi Automatic Guided Vehicle berbasis Raspberry Pi

Setiawan

Hidayat²,

Pratomo³

et al. 2023

JNTETI

View full text Add to dashboard Cite

Perkembangan zaman modern di sektor teknologi robotika dan mekanisasi telah meningkat sangat signifikan dalam beberapa dekade ini karena efisiensinya yang tinggi dari aspek waktu dan tenaga. Pada sistem mobilisasi barang untuk pemanfaatan perusahaan, khususnya bagian industri dan bagian pergudangan, salah satu robot yang digunakan adalah kendaraan berpemandu otomatis pengangkut barang atau yang biasa disebut sebagai automatic guided vehicle (AGV). Salah satu metode navigasi lama di AGV adalah penggunaan sebuah sensor untuk mengikuti pola garis pada objek yang terdeteksi, yaitu garis pada lantai. Metode tersebut kurang efektif karena lambat laun objek pola garis di lantai tersebut akan menghilang akibat efek dari gaya gesek roda AGV, sehingga tidak lagi dapat terdeteksi oleh sensor kamera. Oleh sebab itu, diperlukan sebuah peningkatan metode navigasi AGV agar dapat menjadi sebuah inovasi yang berkelanjutan. Metode navigasi ini menggunakan empat objek gambar yang diposisikan pada area yang dilintasi robot AGV dan kamera sebagai sensor yang menghadap ke depan, sehingga AGV dapat dengan presisi mendeteksi pola objek gambar menggunakan bantuan computer vision menggunakan library perangkat lunak OpenCV. Selanjutnya, pola objek gambar yang sudah terdeteksi diproses oleh sebuah program yang dirancang pada perangkat komputer mini Raspberry Pi 4 Model B. Hasil pengujian membuktikan bahwa metode ini mampu mendeteksi objek gambar yang berada di area yang terjangkau kamera dan berhasil menampilkan keluaran dari objek gambar tersebut. Sistem berhasil mengenali objek secara cukup akurat, dengan parameter jarak 10–95 cm dan melalui beberapa percobaan. Analisis kecepatan putaran roda depan dan belakang AGV dilakukan menggunakan osiloskop dan takometer sebagai alat pengukur kecepatan atau rotasi roda.

show abstract

Faster RCNN Detection Based OpenCV CSRT Tracker Using Drone Data

Cited by 20 publications

References 2 publications

Developing an Automated Detection, Tracking, and Analysis Method for Solar Filaments Observed by CHASE via Machine Learning

Developing an Automated Detection, Tracking, and Analysis Method for Solar Filaments Observed by CHASE via Machine Learning

Smart Fall Detection Framework Using Hybridized Video and Ultrasonic Sensors

Pengenalan Tanda Arah untuk Navigasi Automatic Guided Vehicle berbasis Raspberry Pi

Contact Info

Product

Resources

About