[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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258 H. Josiński et al. paths classification: statistical, histogram, Fourier transform and timeline. The features can be calculated by using dimensionality reduction techniques [24]. The classical approaches of dimensionality reduction of motion data do the work at the level of the pose descriptors. In [19] on the basis of distances between the selected body parts, the feature vectors of binary silhouettes are extracted and the first two principal components are chosen. In [10] linear PCA and in [15] nonlinear manifold learning is applied prior to the classification with HMM. In [21] and [19] DTW follows by the reduction of pose descriptors by PCA of feature vectors calculated for video and motion capture data, respectively. In [18] a modified ICA is used for skeletons of binary silhouettes. Other examples can be found in [12], [8] and [11]. Recently the multilinear reduction methods for tensor objects have gained more attention. They allow to reduce multi-dimensional objects indexed by multiple indices. The motion sequences are addressed by the frame number and spatial coordinates, which means that the entire sequences can be reduced, not only the pose descriptors. In [16] the MPCA method is used for the detected cycles of binary silhouettes and the classification is performed by selected distance functions. The MPCA reduction is also extended by LDA method. The application of MPCA to the classification of motion capture data by supervised learning can be found in [22]. In [14] mutlilinear ICA and in [17] uncorrelated MPCA are applied to face recognitions. It is very challenging task to propose small number of features without loss of individual data. Thus, feature vectors are usually defined in high dimensional spaces, which makes the classification more difficult and less reliable. However, it is still possible to discover a feature subset sufficient for precise motion data classification by a proper search method. That is main contribution of presented research. We apply supervised feature selection for extracted features of video sequences. The effectiveness of selection is evaluated by obtained compression rate and accuracy of a classification. On the basis of our previous work [6] we decided to extract features of video recordings data by MPCA technique and classify them by supervised machine learning. To reduce the dimensionality of feature space and discover most informative ones the selection is carried out, prior to classification phase. Similar as in [6] CASIA gait database is used to examine proposed identification method. 2 Gait Data Representation Portions of the research in this paper use the CASIA Gait Database collected by the Institute of Automation, Chinese Academy of Sciences. The database contains raw video recordings as well as extracted binary silhouettes by background subtraction (see Fig. 1). The data could be used as a basis for markerless motion capture [23].
Selection of Individual Gait Features Extracted by MPCA 259 Fig. 1. Gait sequence from the CASIA database – actor “ljg” CASIA dataset A is chosen. It contains 240 sequences of gaits coming from 20 subjects, recorded from three different camera perspectives: i.e. parallel, 45 degrees and 90 degrees to the image planes. Subjects walk forward in two opposite directions. To improve the reduction and obtain directly comparable MPCA components, which simplifies classification, gaits are unified. We select only the parallel view and reflected horizontally gaits of opposite direction. To make classification independent from gait location and to remove useless data, a bounding box of every video frame is determined. It has fixed 100x180 resolution, adjusted to the tallest subject, and is centered to silhouette geometric centroid. Gait sequences are considered to be the 3rd-order tensors with modes determined by spatial coordinates and time domain. The MPCA requires the same mode dimensionality of all tensors which is satisfied for spatial coordinates, but because of a different number of frames of video recordings the time domain mode has to be normalized. We applied linear scaling with a number of frames determined by an average video size. 3 Gait Identification Performed by Human To prove the thesis of human ability to identify persons on the basis of visual gait analysis, the experiment was carried out. The disjoint training and test sets, containing gaits of four different persons were selected from CASIA dataset A. We asked the group of ten volunteers to recognize gaits. At the first stage, the volunteers tried to notice and remember individual gait features on the basis of a prepared training set. After the training phase, the recognition was performed. The test set consisted of four gaits, exactly one sample of every actor which are different than the ones of the training set. The gaits visualizations in training and testing phases are repeated depending on the necessity of the volunteers. The visualization is done on the basis of determined binary silhouettes as shown in Fig. 2.
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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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