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Vehicle Detection and Tracking in Surveillance

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Vehicle Detection and Tracking in Surveillance

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    1. Vehicle Detection and Tracking in Surveillance University of Central Florida Andrew Miller, Brandyn White, Arslan Basharat, Jingen Lieu, and Dr. Mubarak Shah

    2. Overview Code Split Knight System (+ SVM classification) White Knight (PSO and ICD) Abstractions, Python Interface

    3. Earlier Work KNIGHT System Object Detection (Background Subtraction) Multi-frame Correspondence Tracking Classification (Recurrent Motion) Shadow Removal

    4. Automatic Parameter Tuning Prepare ground-truth segmentations A particle’s position represents a parameter configuration Use swarm equations to update particles Social and Cognitive forces

    5. Illumination Change Detection Problem: illumination changes cause long-term artifacts Linear regression over time (30 frames) Correlation coefficient indicates trend of illumination change Temporarily increase learning rate

    6. SVM Classification Nine-dimensional feature vector Eight edge-orientation histogram bins Aspect ratio Linear classifier

    7. Results VACE Core (September 2006) Mean MOTA: Mean MOTA: 51.5% (Unofficial result) CLEAR (May 2007) (KNIGHT) Mean MOTA: 53.3% MOTP: 55.9% (WKNIGHT) Mean MOTA: 22.5% MOTP: 63.7% Didn’t finish coding White Knight Simple tracking and classification How much does background subtraction matter? The goal of evaluations is to learn how to answer this question rapidly and methodically

    8. Results: MOTA Boxplots Statistically Significant? High degree of per-sequence variation

    9. Results: Per-Sequence Comparison? Precision vs Accuracy Mean Absolute Difference vs Mean Difference?

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