[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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242 H. Josiński et al. The previously computed CCR values were averaged for PCA as well as for each LLE variant separately, with regard to the given value of d (see Fig. 9) and to the given value of N (see Fig. 10). The complete range of N was taken into account, whereas the set of dimensionalities was limited to powers of 2 from 4 to 256. Fig. 9. Dependency between average CCR and dimensionality d for PCA and all LLE variants Fig. 9 shows that the 100 % accuracy was obtained for the reduced dimensionality d = 256 for both methods with the exception of the LLE variant using k = 80 nearest neighbors (avg(CCR) = 95 %). As it was aforementioned, according to expectations the accuracy remained equal to 100 % for d = 512 regardless of the variant of dimensionality reduction. It is worthwhile mentioning that the accuracy of the GP-LVM and HMM-based classification of the CASIA gait sequences, reported in [21] was equal to 86 %. However, in this case the analyzed sequences were not limited to the “parallel-oriented” subset. Although in the 3 following cases: N = 2, 3, 5 accuracy obtained by the PCA was equal to 100 % already for d = 16, but taking the average accuracy into consideration the LLE outperforms the PCA. In particular, the maximum average value of the CCR equal to 88.57 % was achieved for N = 7 states by means of the LLE using k = 40 nearest neighbors. Admittedly, it was mentioned in [25] that the LLE algorithm can only be expected to recover embeddings whose dimensionality is strictly less than the number of neighbors (d < k), but the results of classification of the CASIA gait sequences show that in opposite case the reconstruction error is small enough to obtain acceptable classification accuracy. This observation confirms the remark that the exact relation between k and the faithfulness of the resulting embedding remains an important open question [25].
Feature Extraction and HMM-Based Classification of Gait Video Sequences 243 Fig. 10. Dependency between average CCR and number N of states for PCA and LLE variants 4 Conclusion The average values presented in Fig. 9 and Fig. 10 suggest that the non-linear dimensionality reduction method (LLE) is superior to the linear technique (PCA) in application for classification of gait sequences. Generally, first of all just video and image data sets are supposed to exhibit low-dimensional manifold structures [22]. Application of the HMM as classifier of gait sequences was successful, particularly in view of the fact that the classification accuracy equal to 100 % was obtained. Conclusions drawn from experiments with video sequences taken from the CASIA Gait Database will be helpful during the next stage of the research which will be based on video material from the municipal surveillance system. Future research will also explore the influence of the feature selection methods on the accuracy of the gait-based identification process. The promising Invasive Weed Optimization (IWO) metaheuristic will be adapted to the searching the feature space for an adequate subset of features. References [1] Boyd, J.E., Little, J.J.: Biometric Gait Recognition. In: Tistarelli, M., Bigun, J., Grosso, E. (eds.) Advanced Studies in Biometrics. LNCS, vol. 3161, pp. 19–42. Springer, Heidelberg (2005) [2] Nixon, M.S., Tan, T.N., Chellappa, R.: Human Identification Based on Gait. Springer (2006)
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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344 Conclusions Control algorithms
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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