Poster Spotlights. Session: Motion and Tracking I: Tracking People, Tue 14 June 2010, 1:40-3:20 pm

Transcription

1 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Motion and Tracking I: Tracking People, Tue 14 June 2010, 1:40-3:20 pm Efficient Extraction of Human Motion Volumes by Tracking Juan Carlos Niebles, Bohyung Han and Li Fei-Fei

2 5 Efficient Extraction of Human Motion Volumes by Tracking Goal: efficiently segment humans from unconstrained video Fast automatic extraction of faces enabled many applications We propose a fully automatic system for spatio-temporal segmentation of moving humans Unconstrained camera motion, body poses, cluttered and dynamic backgrounds Sparse use of top-down extraction guides fast & efficient bottom-up segmentation. Results [Niebles, Han & Fei-Fei CVPR 2010]

3 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Motion and Tracking I: Tracking People, Tue 15 June 2010, 13:40-15:20 pm Multisensor-Fusion for 3D Full-Body Human Motion Capture Gerard Pons-Moll, Andreas Baak, Thomas Helten, Meinard Mueller, Hans- Peter Seidel, Bodo Rosenhahn

4 6 Multisensor-Fusion for 3D Full-Body Human Motion Capture Video-based tracker Video Hybrid tracker Inertial Sensors Just five sensors! Neck Hands Lower-leg Linear System 3D Pose Parameters

5 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Tracking People, Tue 15 June 2010, 1:40-3:20 pm An Object-Dependent Hand Pose Prior from Sparse Training Data H.Hamer, J.Gall, T.Weise, L. Van Gool

6 7 An Object-Dependent Hand Pose Prior from Sparse Training Data? different hands/objects tracking training new cup/hand synthesis

7 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Motion and Tracking I, Tue 15 June 2010, 1:40-3:20 pm Vehicle Detection and Tracking in Wide Field-of-View Aerial Video Jiangjian Xiao, Hui Cheng, Harpreet Sawhney, Feng Han Sarnoff Corporation

8 Vehicle Detection and Tracking in Wide field-of-view Aerial Video

9 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Tracking people, Tue 15 June 2010, 1:40-3:20 pm Multi-Target Tracking by On-Line Learned Discriminative Appearance Models Cheng-Hao Kuo, Chang Huang, and Ram Nevatia (University of Southern California)

10 9 Multi-Target Tracking by On-Line Learned Discriminative Appearance Models Goal Track multiple targets in a real scene Challenges Heavy occlusion, similar appearances Contribution On-line discriminative appearance models(oldams) Approach Detection-based tracker by hierarchical association

11 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Tracking people, Tue 15 June 2010, 1:40-3:20 pm Tracking with Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes Louis Kratz and Ko Nishino

12 10 Tracking with Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes Spatio-Temporal Model of the Crowd s Motion Predicted Local Motion Pattern Distribution of Optical Flow Local Motion Patterns Tracking Results

13 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Motion and Tracking I: Tracking People, Tue 15 June 2010, 13:40-15:20 pm AAM based Face Tracking with Temporal Matching and Face Segmentation Mingcai Zhou, Lin Liang, Jian Sun, Yangsheng Wang

14 11 AAM based Face Tracking with Temporal Matching and Face Segmentation Temporal matching constraint Generalizability, fast motion Optimize Pt to match patches Face segmentation constraint Handle the cluttered background E(p1) > E(p2) feature point Frame t-1 Frame t Face region Cost map Comparison Comparison Frame t-1 Our method Traditional AAM No constraint Remove background Results of real-world videos Our method

15 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Tracking people, Tue 15 June 2010, 1:40-3:20 pm Human Identity Recognition In Aerial Images Omar Oreifej Ramin Mehran Mubarak Shah University Of Central Florida

16 12 Problem Human Identity Recognition in Aerial Images Annotated Target Query Image? Proposed Method Detection Blob Extraction Blob Alignment Region Weighting Matching Max[ P( Match)]

17 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Motion and Tracking I: Tracking People, Tue 15 June 2010, 1:40-3:20 pm Silhouette Transformation based on Walking Speed for Gait Identification Y. Makihara, A. Tsuji, Y. Yagi

18 13 Silhouette Transformation based on Walking Speed for Gait Identification Overview Gait is biometrics at a distance DNA Finger print Iris Fac e Gait Issues: Intra-class variation by speed change Near Distance Far Slow Speed Fast Matching with speed transformation Reference 3 km/h Transformed 7km/h Matching Probe 7km/h

19 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Tracking people, Tue 15 June 2010, 1:40-3:20 pm PROST - Parallel Robust Online Simple Tracking Jakob Santner, Christian Leistner, Amir Saffari, Thomas Pock, Horst Bischof

20 14 PROST - Parallel Robust Online Simple Tracking What? Increase the Robustness and Adaptivity of Classifier-based Trackers How? Simple Combination of Trackers with different Adaptivities

21 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Motion and Tracking 1, Tue 15 June 2010, 13:40-15:20 pm Player Localization Using Multiple Static Cameras for Sports Visualization Raffay Hamid, Ram Kumar, Kihwan Kim, Matthias Grundmann, Irfan Essa, Jessica Hodgins

22 15 Player Localization Using Multiple Static Cameras for Sports Visualization View 1 View 2 View 3 Maximally Greedy Benefit: Efficiency Cost: No Guarantee for optimality Optimality-vs-Efficiency Tradeoff Exhaustive Search Benefit: Optimal Solution Cost: Low Efficiency No. of Frames Individual Camera Average Accuracy Naïve Fusion Accuracy Our Method 60, % 75.7% 93.6%

23 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Motion and Tracking, Tue 15 June 2010, 13:40-15:20 pm An Online Approach: Learning-Semantic- Scene-by-Tracking and Tracking-by- Learning-Semantic-Scene Xuan Song, Xiaowei Shao, Huijing Zhao, Jinshi Cui, Ryosuke Shibasaki and Hongbin Zha

24 16 An Online Approach: Learning-Semantic-Scene-by- Tracking and Tracking-by-Learning-Semantic-Scene Problem: (1) How to maintain the correct tracking in a high density scene? a high density and dynamic scene (2) How to learn the semantic scene via an online manner? Learning Semantic Scene by Tracking: Dynamic: crowd flow, density distribution, velocity distribution Static: walk paths, sinks/sources Tracking by Learning Semantic Scene:

25 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Tracking people, Tue 15 June 2010, 1:40-3:20 pm Tracking People Interacting with Objects Hedvig Kjellström Danica Kragic Michael J Black KTH Sweden Brown University USA

26 17 Tracking People Interacting with Objects With object context Without object context

27 The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Poster Spotlights Session: Tracking People, Tue 15 June 2010, 13:40-15:20 Real Time Motion Capture using a Single Time-Of-Flight Camera Varun Ganapathi, Christian Plagemann, Daphne Koller, and Sebastian Thrun

28 18 Real Time Motion Capture using a Single Time-Of-Flight Camera The Problem: Difficulties: Self-occlusions Noise 48 DoF Depth Images at 30hz Pose in Real Time Our Solution: 1. Detect Body Parts 2. Generate Hypotheses Probabilistic inverse kinematics hierarchical sampling 3. Evaluate Error GPU-accelerated highly parallel