Optimized object tracking technique using Kalman filter

Taylor, Liana Ellen; Mirdanies, Midriem; Saputra, Roni Permana

doi:10.14203/j.mev.2016.v7.57-66

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…To get the accurate trajectory of the target, the Kalman filter is applied to the centroid of the human target bounding box. Based on value predictions and the new location of the center, the Kalman filter method calculates the center's location [43]. Prediction and correction are divided into two main stages.…”

Section: Kalman Filter For Accuracy Trajectorymentioning

confidence: 99%

Study on Tracking Real-Time Target Human Using Deep Learning for High Accuracy

Nguyen,

Chu

2023

Journal of Robotics

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Speed and accuracy are important parts of the human tracking system. To design a system that tracks the target human working well in real time, as well as on mobile devices, a tracking real-time target human system is proposed. First, real-time human detection is performed by the combination of MobileNet-v2 and single-shot multibox detector (SSD). Subsequently, the particle filter algorithm is applied to track the target human. The proposed system is evaluated with the different color shirts and complex background conditions. In addition, the system also works with the support of a depth Kinect-v2 camera to evaluate performance. The experiment result indicates that the proposed system is efficient without the impact of colors, background, and light. Moreover, the system still tracks the human when the human has disappeared or the size of the target has a significant change, and an FPS of 12 (Kinect-v2 camera) and 22 (conventional camera) ensures the system works well in real time.

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Section: Kalman Filter For Accuracy Trajectorymentioning

confidence: 99%

Study on Tracking Real-Time Target Human Using Deep Learning for High Accuracy

Nguyen,

Chu

2023

Journal of Robotics

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“…However, an observation was made indicating that despite minor parameter adjustments yielding initial successes, the accuracy of object-detection appeared to falter when the objects displayed notable size variations. In response to this shortcoming, the ElectroNet concept was introduced, aspiring to augment the recognition capabilities of electric-circuit elements across all sizes, whilst maintaining high precision ( [10][11][12][13][14]). This segment of the research takes a meticulous look at the challenges involved in size variations in object detection, and the potential role of ElectroNet in effectively addressing these issues.…”

Section: Introductionmentioning

confidence: 99%

ElectroNet: An Enhanced Model for Small-Scale Object Detection in Electrical Schematic Diagrams

Uzair,

Chai,

Rassau

2023

Preprint

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This study presents the design and development of a highly accurate and efficient approach for interpreting both hand-drawn and printed electric-circuit schematics. It addresses critical challenges in the field such as the lack of annotated data forobject-detection in schematics. To mitigate this, we have created an extensive dataset, comprising of 23 classes of electric-circuit elements, textual elements with units, prefixes, decimal points, and an array of various handwriting and printing styles with a total of 198495 annotations in the dataset. This rich and diverse dataset ensures the robustness of our system against different styles of circuit drawings. We combined our dataset with the MNIST dataset to enhance the model’s resilience to diverse writing styles. Another obstacle faced in this domain is the accurate recognition and assignment of textual labels in schematics. To overcome this, we employed object-detection methodologies, leveraging use of hybrid model. It has proven effective in analyzing line diagrams and incorporating various language OCRs. Despite initial success with minor parameter adjustments, a shortfall in the accuracy of object-detection was identified when significant size variations occurred between objects. To address this, we trained a new model, leading to significant improvements in the recognition of circuit elements and textual symbols across all sizes, with high precision. This enhanced system showed an overall correct simulation rate of 92%, and an overall correct recognition rate of 94% for textual symbols. This work establishes the efficacy of employing object-detection techniques for interpreting electric schematics while also underlining the challenges and potential avenues for future research in this domain. Our approach has shown notable improvements in recognizing electric-circuit drawings, particularly for smaller components, and opens avenues for further advancements in the field of circuit recognition and analysis.

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