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Human action recognition optimization based on evolutionary feature


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Human action recognition constitutes a core component of
advanced human behavior analysis. The detection and recognition of basic human motion enables to analyze and understand human activities, and to react proactively providing diff erent kinds of services from human-computer interaction to health care assistance. In this paper, a feature-level optimization for human action recognition is proposed. The resulting recognition rate and computational cost are signifi cantly improved by means of a low-dimensional radial summary feature and evolutionary feature subset selection.
The introduced feature is computed using only the contour
points of human silhouettes. These are spatially aligned
based on a radial scheme. This defi nition shows to be profi cient for feature subset selection, since di fferent parts of the human body can be selected by choosing the appropriate feature elements. The best selection is sought using a genetic algorithm. Experimentation has been performed on the publicly available MuHAVi dataset. Promising results are shown, since state-of-the-art recognition rates are considerably outperformed with a highly reduced computational cost.

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Human action recognition optimization based on evolutionary feature

  1. 1. Amsterdam, The Netherlands July 06-10, 2013 Real World Applications: RWA4. Room: 02A00 10:40 – 12:20 Session Chair: Alexandros Andre Chaaraoui (University of Alicante, Spain)
  2. 2. ALEXANDROS ANDRE CHAARAOUI AND FRANCISCO FLÓREZ-REVUELTA … … Amsterdam, July 6-10, 2013 Geneticand Evolutionary Computation Conference2013
  3. 3. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) Contents 3 1. Introduction 2. Radial Summary Feature 3. Evolutionary Feature Subset Selection 4. Human Action Recognition Method 5. Experimentation & Results 6. Conclusions 7. References Q & A and Discussion
  4. 4. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 1. Introduction 4  Motivation and starting point Recognition of actions such as walking, jumping or falling. Requirements: High and stable recognition rates Real-time suitability Proposal of a visual feature with reduced extraction cost and low dimensionality Feature subset selection
  5. 5. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 2. Radial Summary Feature 5  Human Silhouettes  Relatively simple extraction process  Rich shape information  Contour points  Radial Summary feature proposal  Spatial alignment  Feature selection  Low dimensionality, reduced extraction cost, … Fig 1: Sample silhouette of the MuHAVi dataset [1].
  6. 6. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 2. Radial Summary Feature 6 Fig 2: Overview of the proposed Radial Summary
  7. 7. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 3. Evolutionary Feature Subset Selection7 
  8. 8. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 4. Human Action Recognition Method8
  9. 9. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 4. Human Action Recognition Method9  Learning based on Bag-of-Key-Poses Model The available pose representations are reduced to a representative subset of key poses We use the K-means clustering algorithm
  10. 10. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 4. Human Action Recognition Method10  Sequence recognition  Sequences of key poses  Nearest-neighbour key poses  Sequence matching (dynamic time warping) Fig 3: Sequences of key poses.
  11. 11. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 5. Experimentation & Results 11  Tested on the MuHAVi-MAS Dataset [1]  Two versions with 14 and 8 actions  Manually Annotated Silhouettes  Leave-one-actor-out (LOAO) and leave-one-sequence out (LOSO) cross validations Dataset Test Chaaraoui et al. [2] Radial Summary Feature Selection State of the Art Rate [3] MuHAVi-14 LOSO 94.1% 95.6% 98.5% 91.9% MuHAVi-14 LOAO 86.8% 91.2% 94.1% 77.9% MuHAVi-8 LOSO 98.5% 100% 100% 98.5% MuHAVi-8 LOAO 95.6% 97.1% 100% 85.3%
  12. 12. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 5. Experimentation & Results 12  Result of the feature selection  ~47% feature size reduction  ~14% temporal reduction  96 FPS overall recognition rate Fig 4: Resulting feature subset selection of the MuHAVi-14 LOSO cross validation test (dismissed radial bins are shaded in gray).
  13. 13. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 6. Conclusions 13  Conclusions  An evolutionary algorithm has been applied to optimize action recognition.  An appropriate feature for feature subset selection has been proposed.  We demonstrated that a guided selection of feature elements can improve the recognition rate and reduce the computational cost.  Future work  Real-valued weights instead of binary selection  Action-class specific feature selection
  14. 14. ©AlexandrosAndreChaaraouiandFranciscoFlórez-Revuelta(GECCO’13) 7. References 14  [1] Singh, S., Velastin, S.A., Ragheb, H.: Muhavi: A multicamera human action video dataset for the evaluation of action recognition methods. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 48–55 (2010)  [2] Chaaraoui, A.A., Climent-Perez, P., Florez-Revuelta, F.: An Efficient Approach for Multi-view Human Action Recognition based on Bag-of-Key-Poses. In Salah, A., ed.: Human Behavior Understanding. Lecture Notes in Computer Science. Springer Berlin / Heidelberg (2012)  [3] A. Eweiwi, S. Cheema, C. Thurau, and C. Bauckhage. Temporal key poses for human action recognition. In Computer Vision Workshops (ICCV Workshops), IEEE International Conference on, pp. 1310-1317 (2011)
  15. 15. Q & A and Discussion15
  16. 16. ALEXANDROS ANDRE CHAARAOUI AND FRANCISCO FLÓREZ-REVUELTA … … Amsterdam, July 6-10, 2013 Geneticand Evolutionary Computation Conference2013