Selection of Histograms of Oriented Gradients Features for Pedestrian Detection

Kobayashi, Takao; Hidaka, Akinori; Kurita, Takio

doi:10.1007/978-3-540-69162-4_62

Cited by 89 publications

(56 citation statements)

References 12 publications

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“…They used 100 samples as training data and 100 samples as a test. The feature extraction results were trained using the Support Vector Machine (SVM) method [4], [5]. In their research, they managed to achieve 95% success in the detection of victims automatically.…”

Section: Related Workmentioning

confidence: 99%

“…Takuya Kobayashi, et al [5], from the University of Tsukuba and the Institute of Advanced Industrial Science and Technology (AIST), Japan. They research about pedestrian features by using the HOG feature extract [4].…”

Section: Related Workmentioning

confidence: 99%

“…An excellent method for detecting human body to date is SVM and HoG [4]. This method has a good performance to detect the human body like a pedestrian [5] and disaster victims [6]. The model used greatly affects the end result of the detector.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance

Salfikar

Sulistijono

Basuki

2018

emitter

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Finding victims at a disaster site is the primary goal of Search-andRescue (SAR) operations. Many technologies created from research for searching disaster victims through aerial imaging. but, most of them are difficult to detect victims at tsunami disaster sites with victims and backgrounds which are look similar. This research collects post-tsunami aerial imaging data from the internet to builds dataset and model for detecting tsunami disaster victims. Datasets are built based on distance differences from features every sample using Histogram-of-Oriented-Gradient (HOG) method. We use the longest distance to collect samples from photo to generate victim and non-victim samples. We claim steps to collect samples by measuring HOG feature distance from all samples. the longest distance between samples will take as a candidate to build the dataset, then classify victim (positives) and non-victim (negatives) samples manually. The dataset of tsunami disaster victims was re-analyzed using crossvalidation Leave-One-Out (LOO) with Support-Vector-Machine (SVM) method. The experimental results show the performance of two test photos with 74.36% precision, 81.60% accuracy, 61.70% recall and fmeasure 67.44% to distinguish victim (positives) and non-victim (negatives).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance

Salfikar

Sulistijono

Basuki

2018

emitter

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“…It uses overlapping local contrast normalizations for improving performance. The HOG feature has significant effects for detecting humans and vehicles in conjunction with machine learning algorithms [41][42][43][44][45]. One interesting application used HOG features to detect landmines in ground-penetrating radar [40].…”

Section: Research Backgroundmentioning

confidence: 99%

Artwork Identification for 360-Degree Panoramic Images Using Polyhedron-Based Rectilinear Projection and Keypoint Shapes

Jin

Kim

2017

Applied Sciences

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Abstract:With the increased development of 360-degree production technologies, artwork has recently been photographed without authorization. To prevent this infringement, we propose an artwork identification methodology for 360-degree images. We transform the 360-degree image into a three-dimensional sphere and wrap it with a polyhedron. On the sphere, several points are located on the polyhedron to determine the width, height, and direction of the rectilinear projection. The 360-degree image is divided and transformed into several rectilinear projected images to reduce the adverse effects from the distorted panoramic image. We also propose a method for improving the identification precision of artwork located at a highly distorted position using the difference of keypoint shapes. After applying the proposed methods, identification precision is increased by 45% for artwork that is displayed on a 79-inch monitor in a seriously distorted position with features that were generated by scale-invariant feature transformations.

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“…Also, these features are likely irrelevant and redundant. PCA was applied in [53,54] for reducing the dimensionality of the feature vectors. PCA can be defined as the orthogonal projection of the input data onto a lower dimensional linear subspace, such that the variance of the projected samples is maximized.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%