2.Our Framework 2.1. Enforcing Temporal Consistency by Post Processing  Human Detection from Yang and Ramanan [1] Articulated Pose Estimation using Flexible Mixtures of Parts. Human Detection in Videos using Spatio-Temporal Pictorial Structures Amir Roshan Zamir, Afshin Dehghan, Ruben Villegas University of Central Florida 1.Problem  Human Detection in Videos:  Making Human Detection in Videos more Accurate.  Possibility of Numerous False Detections.  Applications:  Video surveillance, Human Tracking, Action Recognition, etc. 5.Conclusion 6. Temporal Part Deformation Improves Human Detection in Videos Based on Our Experiments.  Less False Detections and More True Detections.  Part Trajectories are More Precise. 3.Learning Transition of Parts  Human Body Parts Have a Set Range of Motion that Can Be Approximated.  These Movements(Trajectories) Can Be Learned by Training on the Annotated Dataset.  We will Use the HumanEva Dataset [2] for Our Training. 1.1Our Approach  Using Temporal Information (Transition of Human Parts in Pictorial Structures).  False Detections Should Be Temporally Less Consistent than True Detections.  Human Parts Transition Should Convey Information Which is Ignored in the Frame-By-Frame Scenario. 2.2.Enforcing Temporal Consistency by Embedding them into the Detection Process  Our Contribution:  Extending Spatial Pictorial Structures to Spatio-Temporal Pictorial Structures. Temporal Deformation Cost 1 23 Frame Number : i i i Configuration of parts Appearance Spatial Deformation Cost  More Elegant Approach than Post Processing(2.1).  Best Detections Are Determined During the Optimization Process.  Configuration of Parts are Limited to Transitions in Time (Temporal Deformation).  This Transitions will Be Learned and Embedded in Our Optimization Process to Restrict the Detections. Part’s Trajectories Before Temporal Adjustment Part’s Trajectories After Temporal Adjustment Next Steps  Applying the Temporal Deformation Cost in the Optimization Process.  Train a Model that Considers Usual Human Part Transitions in Time. Part’s Trajectories on Video Parts Trajectories of Annotations Head Trajectory Before Temporal Adjustment Head Trajectory After Temporal Adjustment Annotated Parts in each frame Head Trajectories Comparison Input Frame Compute Human Detection Pick a Bundle of n Frames Check Part Transition in the Bundle of Frames Keep Frames that Move Consistently in Time Refine Part Location using Temporal Information Input Frame Immediate Output from Human Detection Temporally Consistent Detection without Part Adjustment Temporally Consistent Detection with Part Adjustment 4.Results 5. Videos Taken From TRECVID MED11 Dataset. Human Detection Output Human Detection Output with Temporal Consistency Human Detection Output with Temporal Consistency and Part Adjustment Human Detection Output with Temporal Consistency Human Detection Output Input Frame References [1] Y. Yang, D. Ramanan. “Articulated Pose Estimation using Flexible Mixture of Parts” Computer Vision and Pattern Recognition (CVPR) Colorado Springs, Colorado, June 2011 [2] L. Sigal, A. O. Balan and M. J. Black. HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulate Human Motion, International Journal of Computer Vision (IJCV), Volume 87, Number 1-2, pp. 4-27, March, 2010.