[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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260 H. Josiński et al. Fig. 2. Training set visualization Detailed results are presented in Tab. 1. The total average precision in the meaning of percentage of correctly identified gaits is 45%. It is noticeably better than random guessing which has only 25% in case of four actors. Most of the volunteers properly classified two gaits and missclassified other two ones, beside volunteers V7 and V8 which have worse precision with only single correctly identified gait. The standard deviation calculated for subsequent volunteers is 11% and for subsequent gaits is 26%. Table 1. Identification performed by human, experiment 1 V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 AVG FYC 1 0 1 1 1 0 1 1 1 1 80% HY 0 1 1 1 0 0 0 0 0 0 30% LJG 0 0 0 0 0 1 0 0 1 0 20% LQF 1 1 0 0 1 1 0 0 0 1 50% AVG 50% 50% 50% 50% 50% 50% 25% 25% 50% 50% 45% To examine gait discrimination of the following actors separately - maybe some of them are very easy to recognize but others are difficult, we determine confusion matrix and split the task into four two class problems, similar as in authorization challenges. We calculate true positives (TP), true negatives (TN), false positives (FP), false negatives, precision, specificity and sensitivity [13]. The results are presented in Tab. 2 and Tab. 3. In most cases the actor FYC is properly identified, it has noticeably the greatest precision, sensitivity and sensibility. His distinctiveness is probably caused by his long hairs, which are easy for human to recognize. The missclassification of remaining actors HY, LJG and LQF is very similar, they are confused quit randomly. Greater specificity values are mainly the result of more negative samples in the test set. Table 2. Confusion matrix, experiment 1 FYC HY LJG LQF FYC 8 1 1 0 HY 1 3 5 1 LJG 1 3 2 4 LQF 0 3 2 5
Selection of Individual Gait Features Extracted by MPCA 261 Table 3. Two class problems, experiment 1 TP TN FP FN Precision Sensitivity Sensibility FYC 8 28 2 2 90% 93% 80% HY 3 23 7 7 65% 77% 30% LJG 2 22 8 8 60% 73% 20% LQF 5 25 5 5 75% 83% 50% The obtained average identification precision is definitely better in comparing to random guessing, however it seems that selected gaits are very discriminative and should allow for more accurate identification. That is a reason we repeated experiment with simultaneous presentation of the training and test sets. It means, that volunteers observed and analyzed all gaits in the same moment and their task was only to match samples of the training and test sets. In such a case, the identification is independent on human abilities to memorize noticed individual gait features and relies only the observed gait similarities. Table 4. Identification performed by human, experiment 2 V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 AVG FYC 1 1 1 1 1 1 1 1 1 1 100% HY 1 1 1 1 1 1 1 1 1 1 100% LJG 1 0 1 1 1 1 1 1 1 1 90% LQF 1 0 1 1 1 1 1 1 1 1 90% AVG 100% 50% 100% 100% 100% 100% 100% 100% 100% 100% 95% The results of identification with simultaneous presentation of the training and test sets are presented in Tab. 4, Tab. 5 and Tab. 6. This time they are very close to the ideal case of 100% of classification accuracy, only single volunteer V2 mistook actors LJG and LQF. As we supposed humans are easily able to recognize persons on the basis of their gait inspection, but more challenging task is to keep in mind discovered individual gait features. Probably longer teaching phase would improve the results of the experiment 1. The number of persons to identify by human is obviously limited, because of restriction of simultaneous presentation and observation. On the basis of the experiment performed we can state the thesis that gait allows to high precision human identification. Table 5. Confusion matrix, experiment 2 FYC HY LJH LQF FYC 10 0 0 0 HY 0 9 1 0 LJG 0 1 9 0 LQF 0 0 0 10
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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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6 A. Babiarz et al. The description
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8 A. Babiarz et al. a) b) Fig. 5. T
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10 A. Babiarz et al. o o o “micvo
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12 A. Babiarz et al. a) b) Fig. 7.
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14 A. Babiarz et al. • The camera
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16 A. Babiarz et al. the camera ser
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18 A. Babiarz et al. a) b) Fig. 11.
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Recognition and Location of Objects
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116 D. Bereska et al. Presented in
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118 D. Bereska et al. Fig. 2. Image
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120 D. Bereska et al. Fig. 4. Ortho
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Omnidirectional Video Acquisition D
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Part III Design of Vision Based Con
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Vision System for Group of Mobile R
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184 D. Bereska et al. 4.1 Mechanica
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186 D. Bereska et al. 4.1.3 Open-Lo
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Probabilistic Approach to Planning
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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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