發佈於 2013-11-18
觀看次數: 926
  • 00:38 1.
    Tracking Pedestrians Using Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes
  • 00:05 2.
    Outline
  • 01:31 3.
    Motivation
  • 02:13 4.
    Introduction(1)
  • 00:50 5.
    Motivation
  • 00:07 6.
    Observation
  • 01:02 7.
    Introduction(2)
  • 01:09 8.
    Spatio-temporal motion pattern
  • 00:36 9.
    Spatio-temporal motion pattern
  • 08:08 10.
    Introduction(3)
  • 01:16 11.
    Introduction(4)
  • 00:18 12.
    Introduction(3)
  • 01:35 13.
    Introduction(4)
  • 00:01 14.
    Introduction(5)
  • 00:15 15.
    Introduction(4)
  • 00:37 16.
    Introduction(5)
  • 00:03 17.
    Introduction(4)
  • 00:16 18.
    Introduction(5)
  • 00:03 19.
    Introduction(4)
  • 00:29 20.
    Introduction(5)
  • 00:03 21.
    Proposed method
  • 00:51 22.
    Flow chart
  • 00:04 23.
    Slide 14
  • 00:52 24.
    Step (a)-statistical model for motion patterns
  • 00:33 25.
    Slide 16
  • 01:54 26.
    Step (b)-train hidden Markov models
  • 00:01 27.
    Slide 16
  • 00:01 28.
    Step (a)-statistical model for motion patterns
  • 00:01 29.
    Slide 14
  • 00:42 30.
    Flow chart
  • 00:01 31.
    Step (a)-statistical model for motion patterns
  • 01:02 32.
    Step (b)-train hidden Markov models
  • 01:50 33.
    Step(c)- predict motion patterns
  • 01:03 34.
    Step(d)-trace individuals
  • 01:20 35.
    Slide 20
  • 00:12 36.
    Slide 21
  • 00:14 37.
    priors
  • 00:38 38.
    Step(d)-trace individuals
  • 00:01 39.
    Slide 21
  • 00:19 40.
    priors
  • 00:05 41.
    Transition distribution
  • 00:01 42.
    priors
  • 00:52 43.
    Slide 20
  • 01:09 44.
    Step(d)-trace individuals
  • 00:01 45.
    Slide 21
  • 00:51 46.
    Transition distribution
  • 00:45 47.
    Likelihood distribution
  • 00:02 48.
    Slide 25
  • 00:18 49.
    Likelihood distribution
  • 00:05 50.
    Slide 25
  • 00:03 51.
    Likelihood distribution
  • 01:12 52.
    Slide 25
  • 00:05 53.
    Likelihood distribution
  • 00:23 54.
    Slide 25
  • 01:39 55.
    Slide 26
  • 00:01 56.
    Experimental results
  • 00:03 57.
    Slide 26
  • 00:03 58.
    Slide 25
  • 01:09 59.
    Likelihood distribution
  • 00:19 60.
    Slide 25
  • 02:13 61.
    Slide 26
  • 00:08 62.
    Slide 25
  • 00:02 63.
    Slide 26
  • 00:12 64.
    Experimental results
  • 00:06 65.
    Datasets
  • 00:03 66.
    Datasets
  • 00:07 67.
    Datasets
  • 00:01 68.
    Datasets
  • 00:16 69.
    Datasets
  • 00:17 70.
    Datasets
  • 00:02 71.
    Datasets
  • 00:03 72.
    Datasets
  • 00:22 73.
    Experiment 1
  • 00:01 74.
    Datasets
  • 00:02 75.
    Datasets
  • 00:01 76.
    Datasets
  • 00:33 77.
    Experiment 1
  • 00:04 78.
    Datasets
  • 00:01 79.
    Datasets
  • 00:07 80.
    Experiment 1
  • 00:11 81.
    Datasets
  • 00:06 82.
    Experiment 1
  • 00:04 83.
    Datasets
  • 00:02 84.
    Datasets
  • 00:01 85.
    Datasets
  • 00:27 86.
    Datasets
  • 00:04 87.
    Experimental results
  • 00:16 88.
    Datasets
  • 00:01 89.
    Datasets
  • 00:06 90.
    Experiment 1
  • 00:16 91.
    Datasets
  • 00:15 92.
    Experiment 1
  • 00:10 93.
    Datasets
  • 00:02 94.
    Experiment 1
  • 00:56 95.
    Experiment 2
  • 00:06 96.
    Slide 32
  • 00:04 97.
    Experiment 2
  • 00:49 98.
    Experiment 3
  • 02:12 99.
    Slide 34
  • 02:08 100.
    Experiment 4
  • 01:36 101.
    Experiment 5
  • 00:31 102.
    Experiment 6
  • 00:32 103.
    Conclusion
  • Index
  • Note
  • Discuss
  • Fullscreen
Tracking Pedestrians Using Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes
58:20, 發佈於 2013-11-18 by VCLab NTHU