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Critical Features for Recognition of Biological Motion

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Critical Features for Recognition of Biological Motion: a presentation of Casile & Giese (2005) paper

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Critical Features for Recognition of Biological Motion

  1. 1. Critical features for the recognition of biological motion Casile & Giese (2005) Your Name Your Title Your Organization (Line #1) 2005-12-31 Your Organization (Line #2)
  2. 2. Introduction Point-light stimuli experiments Perception of complex biological movements (Johansson, 1973) Not impaired By adding noise (Cutting et al. 1988) By changing the contrast polarity of the dots Ahlström et al.1997) If only a subset of dots is visible If the dots are displaced on the skeleton in every frame Stimulus Perception 2
  3. 3. Introduction Different Hypotheses Hypothesis 1: Computational mechanisms reconstruct the missing information from impoverished stimuli by fitting a skeleton model to the stimuli (dots) most of the existing algorithms are computationally expensive and have no obvious neural implementation Hypothesis 2: Generalization from normal to point-light stimuli is based on specific features that are shared by both stimulus classes. The nature of such features is largely unknown It has been discussed whether they are based on form or motion information Motion Form 3
  4. 4. Method Two movies Stickman walking Moving dots Stimulus Optic Flow 4
  5. 5. Analysis and Results Two movies Stickman walking Moving dots Stimulus r=0.09 PCA Optic Flow r=0.93 The dominant motion features are very similar for both stimuli, but the dominant form features are different. 5
  6. 6. Psychophysical experiments CFS Stimulus Object 1 Direction Motion Information Random Dots 6
  7. 7. Motion Information Experiment 1 Random Dots 1A Arrangement Asymmetric CFS Stimulus Written report about their perceptual impression 13/17: “Human walking” 4/17: “jumping dots” or “nothing” direction 1B Symmetric CFS Stimulus 2/9: “Human Walking” 4/9: “Human performing actions” 3/9 “Nothing” or “The Number 8” 1C Random dots 2/10: “Human Performing actions” 8/10: “Nothing” 7
  8. 8. Experiment 1 Results Opponent motion seems to be critical for generating the impression of a walking human. The presence of moving dots within the same four regions is not sufficient. Skeleton model hypothesis seems to be wrong Coarse position information is not sufficient to fit this model The random dots' positions do not comply with the kinematics of a moving human body Alternative hypothesis: We use fuzzy templates for the human body shape that fit the CFS in a sub-optimal way 8
  9. 9. Experiment 2 Method If reconstruction of the human body shape from point positions is critical for the recognition of point-light walkers, then a stimulus that complies with kinematics should be easier recognized than the CFS stimulus SPS CFS (Sequential Position Stimulus) (Critical Features Stimulus) .... ... .. .. .. ... .. . VS .. . . Frame 1 Frame 2 Frame 3 t SPS does not affect body shape and matches exactly the human body kinematics 1,2,4 dots 1 frame 9
  10. 10. Experiment 2 Results SPS CFS .. . . VS ..... 7 Subjects Task: Recognition of direction of walking No differences between the two stimuli No precise information about the body shape is needed Both stimuli might be processed by a common mechanism Asymmetry of the stimulus seems to be an important factor 10
  11. 11. Neural Model I. Local Motion Detectors (LMD): small receptive fields, direction preference II. Opponent motion detectors: Respond if LMD -within two adjacent subfields- with oposite direction preference are active III. Detectors for complex global optic flow patterns: Larger receptive fields than the whole point-light stimulus, selectivity established by training, each frame has an optic flow pattern that is encoded by a radial basis function IV. Motion Pattern Neurons: Sum and temporally smooth the activities of optic flow pattern detectors that belong to the same human action 11
  12. 12. Neural Model Psychophysical experiment Results Recognition performances for both types of stimuli are very similar. Recognition performance increases with the number of dots in the stimulus Recognition rates for 8 and 4 dots are close to the values obtained in the psychophysical experiment The recognition rates for 2 dots are lower than human performance This model is not able to analyze stimuli with a single dot No strong increase of performance with the lifetime of dots High recognition rates can be accomplished solely based on the proposed critical motion feature High performance rates for degraded stimuli can be accomplished without complex computational mechanisms 12
  13. 13. Discussion Normal and point-light stimuli share very similar dominant mid-level optic flow features r=0.93 The appropriate spatial arrangement of these features induces the percept of a person walking, even though the stimuli do not comply with the kinematics of the human body The detailed form information provided by the SPS does not seem to improve the recognition of walking direction A neural model that exploits these critical features achieves substantial recognition rates, even for degraded point-light stimuli 13
  14. 14. Discussion Physiological studies support this computational model (neural detectors for opponent motion) Simple neural circuit. Not complex computational mechanism. The local motion information can be used for other discrimination tasks (e.g. identification of gait) Fast Slow http://www.biomotionlab.ca/Demos/BMLwalker.html 14
  15. 15. Discussion Physiological studies support this computational model (neural detectors for opponent motion) Simple neural circuit. Not complex computational mechanism. The local motion information can be used for other discrimination tasks (e.g. identification of gait) For more difficult tasks, more information might be required. Female Male http://www.biomotionlab.ca/Demos/BMLwalker.html 15
  16. 16. Thank you! 16

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