Real-time Moving Object Video Tracking using Support Vector Machines for Visual Servo Application

Asali, Modestus Oliver; Saripudin, Saripudin; Indriyanto, Toto; Trilaksono, Bambang Riyanto

doi:10.1109/icaiit.2019.8834491

Cited by 1 publication

(2 citation statements)

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“…With the continuous improvement in smartphone performance and widespread use of deep learning technology, object detection techniques based on smartphones using a cradle servomotor have gained research attention [ 1 , 2 , 3 ]. Real-time moving object detection technology has been widely used in various fields, such as robotics, transportation, manufacturing, and security.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Moving Object Tracking on Smartphone Using Cradle Head Servo Motor

Han,

Ryu,

Nam

2024

Sensors

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The increasing demand for artificially intelligent smartphone cradles has prompted the need for real-time moving object detection. Real-time moving object tracking requires the development of algorithms for instant tracking analysis without delays. In particular, developing a system for smartphones should consider different operating systems and software development environments. Issues in current real-time moving object tracking systems arise when small and large objects coexist, causing the algorithm to prioritize larger objects or struggle with consistent tracking across varying scales. Fast object motion further complicates accurate tracking and leads to potential errors and misidentification. To address these issues, we propose a deep learning-based real-time moving object tracking system which provides an accuracy priority mode and a speed priority mode. The accuracy priority mode achieves a balance between the high accuracy and speed required in the smartphone environment. The speed priority mode optimizes the speed of inference to track fast-moving objects. The accuracy priority mode incorporates CSPNet with ResNet to maintain high accuracy, whereas the speed priority mode simplifies the complexity of the convolutional layer while maintaining accuracy. In our experiments, we evaluated both modes in terms of accuracy and speed.

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Section: Introductionmentioning

confidence: 99%

“…Asali et el. [ 2 ] presented a real-time video-tracking system for visual servo applications using support vector machines. The system aimed to maintain the position of a target object in the FOV of the camera by tracking it in complex environments and lighting conditions.…”

Section: Introductionmentioning

confidence: 99%