DroneSURF: Benchmark Dataset for Drone-based Face Recognition
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...However, only a very few UAV datasets have been constructed so far and most datasets are limited to a specific task, such as visual tracking [e.g., UAV123 (Mueller et al. 2016), UAV123L (Mueller et al. 2016) and Campus (Robicquet et al. 2016)] or detection [e.g., CARPK (Hsieh et al. 2017), DOTA (Xia et al. 2018) and DroneSURF (Kalra et al. 2019)]....
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...…so far and most datasets are limited to a specific task, such as visual tracking [e.g., UAV123 (Mueller et al. 2016), UAV123L (Mueller et al. 2016) and Campus (Robicquet et al. 2016)] or detection [e.g., CARPK (Hsieh et al. 2017), DOTA (Xia et al. 2018) and DroneSURF (Kalra et al. 2019)]....
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"DroneSURF: Benchmark Dataset for Dr..." refers methods in this paper
...Some key observations are as follows: (i) Face Recognition Performance: From the frame-wise identification results reported in Table IV, it can be observed that for all frames, best rank-1 identification performance of 14.36% is obtained with VGG-Face feature descriptor for active surveillance, while an accuracy of 5.08% is achieved with LBP features for passive surveillance....
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...Baselines for face recognition have been computed with two hand-crafted features: (i) Histogram of Oriented Gradients (HOG) [8], (ii) Local Binary Pattern (LBP) [23], two deep learning based feature extractor: (iii) VGG-Face [25], (iv) VGG-Face2 [7], and (v) a Commercial-Off-The-Shelf system (COTS)....
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"DroneSURF: Benchmark Dataset for Dr..." refers methods in this paper
...(ii) Analysis of Face Detection: Tiny Face and Viola Jones detected a total of 131K and 64K faces for active surveillance, while the ground truth annotated faces are a little over 125K....
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...On the other hand, the total number of detected faces by Viola Jones detector is less than half of the total annotated faces....
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...For passive surveillance, Tiny Face and Viola Jones detected a total of 136K and 35K faces, respectively, for the ground truth annotated faces of over 155K....
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...Algorithm Active Surveillance Passive SurveillancePrecision Recall Precision Recall Viola Jones [29] 22.60 27.50 2.15 1.15 Tiny Face [14] 96.52 94.59 95.36 78.80 Fig....
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...For the two face detectors, Viola Jones and Tiny Face, Table III presents the precision and recall values for both scenarios of active and passive surveillance....
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5,308 citations
"DroneSURF: Benchmark Dataset for Dr..." refers methods in this paper
...Some key observations are as follows: (i) Face Recognition Performance: From the frame-wise identification results reported in Table IV, it can be observed that for all frames, best rank-1 identification performance of 14.36% is obtained with VGG-Face feature descriptor for active surveillance, while an accuracy of 5.08% is achieved with LBP features for passive surveillance....
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...With VGG-Face, quality-based frame selection results in increased performance for both the scenarios (14.36% to 16.78%, and 4.66% to 4.95%), as compared to alternate frame selection....
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...At rank-5, VGG-Face descriptor achieves the best performance of around 39% for active surveillance, while the highest performance for passive surveillance is only around 24%....
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...Baselines for face recognition have been computed with two hand-crafted features: (i) Histogram of Oriented Gradients (HOG) [8], (ii) Local Binary Pattern (LBP) [23], two deep learning based feature extractor: (iii) VGG-Face [25], (iv) VGG-Face2 [7], and (v) a Commercial-Off-The-Shelf system (COTS)....
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