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Using Local Temporal Features of Bounding Boxes for Walking/Running Classification

running. 0.07. walking. 0.06. decision boundary. 0.05. PERIOD. SWING. PERIOD. 0.04. 0.03. 0.02. 0.01. 120. 0. 5. 10. 15. 20. 25. 30. Using Local Temporal Features of Bounding Boxes for Walking/Running Classification Berkay Topcu and Hakan Erdogan

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Using Local Temporal Features of Bounding Boxes for Walking/Running Classification

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  1. running 0.07 walking 0.06 decision boundary 0.05 PERIOD SWING PERIOD 0.04 0.03 0.02 0.01 120 0 5 10 15 20 25 30 Using Local Temporal Features of Bounding Boxes for Walking/Running Classification Berkay Topcu and Hakan Erdogan Faculty of Engineering and Natural Sciences, Sabanci University, Orhanli Tuzla 34956, Istanbul, Turkey berkayt@su.sabanciuniv.edu, haerdogan@sabanciuniv.edu Feature Extraction Scatter Plot The Problem • Intelligent Surveillance: Automatic behaviour detection • Security Purposes; airports, metro stations, shopping centers, parks, etc. • Tracking Activity Recognition • Behaviour Understanding • Our aim is to recognize and classify human activities from surveillance videos • Classification of human activities as running or walking • For periodic features, a window of 33 frames is used, so more than one walking cycle is observed. speed An example width/height ratio signal for a video and its autocorrelation period • For derivative features, a window of 7 frames (3 forward, 3 backward and current frame) are used for derivative calculation. Scatter plot of period vs. speed and decision boundary for linear discriminant classifier Bounding Box Approach Classification Results • Bounding boxes are used for feature extraction • The Database • 6 running videos (194 frames) Total : 30 videos • 24 walking videos (1737 frames) (1931 frames) • For speed: An example displacement signal (in x-direction) and the fitted line around current frame Examples of marked video and calculation of speed • Speed is calculated using the displacement of the center point of the bounding box. • All displacement values are normalized for scale. Features Discussion & Conclusion • Features related to period • - Period of width/height ratio signal (PERIOD) • - Swing of width/height ratio signal (SWING) • Temporal derivative features • - Change of width/height (DERIVATIVE) • - Percentage change of width (W-DERIVATIVE) • - Percentage change of height (H-DERIVATIVE) • Speed of the bounding box (SPEED) • Best features: Combination ofPERIOD-SWING-SPEED • Best classifier: QDC (Quadratic classifier assuming normal densities) and LMNC (Neural network classifier trained by the Levenberg-Marquardt rule) • Recognition rate is satisfactory (97.62%) • Simple dynamic features of bounding boxes good enough for classification • Derivative features not as helpful Training and Testing Training: Feature Extraction Raw data from different people Classifiers for running versus walking Classifier Training Testing: Score calculation for each classifier Feature Extraction Unknown raw data Decision of action (walking/running)

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