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The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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Parallel Tracking and Verifying.

Heng Fan, Haibin Ling

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 21, 2019
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    Summary
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    A new Parallel Tracking and Verifying (PTAV) framework uses multi-threading for efficient visual object tracking. This approach balances speed and accuracy, outperforming many deep learning methods in real-time scenarios.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Visual object tracking is vital in computer vision, with recent advances focusing on speed or accuracy.
    • Real-time, high-accuracy tracking remains a significant challenge.

    Purpose of the Study:

    • Introduce a novel Parallel Tracking and Verifying (PTAV) framework.
    • Address the scarcity of real-time and high-accuracy visual tracking algorithms.

    Main Methods:

    • Developed a PTAV framework with two parallel threads: a fast tracker (T) and a verifier (V).
    • The verifier (V) selectively validates and corrects the tracker (T) based on its requests.
    • Implemented a dynamic target template pool for adaptive verification to handle appearance changes.

    Main Results:

    • PTAV achieves top tracking accuracy among real-time trackers.
    • Outperforms many deep learning-based algorithms on benchmark datasets (OTB2015, TC128, UAV20L, VOT2016).
    • Demonstrates high efficiency and strong discriminative power through parallel collaboration.

    Conclusions:

    • The PTAV framework offers a flexible and effective solution for real-time visual object tracking.
    • The parallel design enhances both speed and accuracy.
    • PTAV shows significant potential for future improvements and generalization in computer vision.