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Description: track by usinghistogram of gradients
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Size: 45056 |
Author: mod |
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Description: 图像梯度方向直方图（HOG)特征基础上云模型运动目标检测算法,提出HOG特征为基础的均值漂移算法
-Moving target detection algorithm of image gradient orientation histogram (HOG) features based on cloud model proposed HOG feature based on mean shift algorithm
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Size: 20087808 |
Author: QQ |
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Description: pedestrian tracking/ this post about how to use OpenCV s HOG-based pedestrian detector: How can I detect and track people using OpenCV? ... of opencv. that said it should be easy to hack the opencv source code
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Size: 20480 |
Author: vit |
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Description: 本文主要研究海面运动船只的识别与跟踪技术。首先概述了海上运动目标检测和跟踪的研究现状；对目前主要的显著区域提取、运动目标识别和跟踪方法进行了简要概述；提出了基于视觉注意和HOG特征相融合的海上船只目标检测方法；利用多特征融合的粒子滤波算法对运动目标进行了跟踪。-This paper studies the sea sport vessel identification and tracking technology. First, an overview of maritime moving target detection and tracking research status right now the main salient region extraction, moving target identification and tracking methods are briefly outlined proposed based on visual attention and HOG feature fusion of sea vessels target detection method utilization multi-feature fusion particle filter algorithm to track the moving target.
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Size: 5353472 |
Author: wenping |
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Description: this code for hog describtion in tracking
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Size: 1024 |
Author: hamid |
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Description: 基于kernel方法的 人体检测跟踪，检测效果好，相比HOG快，-kernel people detection and track fast than HOG
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Size: 340992 |
Author: 孙旭光 |
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Description: Read positive and negative training sample image files specified directories
Calculate their HOG features and keep track of their classes (pos, neg)
Save the feature map (vector of vectors/matrix) to file system
Read in and pass the features and their classes to a machine learning algorithm, e.g. SVMlight
Train the machine learning algorithm using the specified parameters
Use the calculated support vectors and SVM model to calculate a single detecting descriptor vector
Dry-run the newly trained custom HOG descriptor against training set and against camera images, if available-Read positive and negative training sample image files specified directories
Calculate their HOG features and keep track of their classes (pos, neg)
Save the feature map (vector of vectors/matrix) to file system
Read in and pass the features and their classes to a machine learning algorithm, e.g. SVMlight
Train the machine learning algorithm using the specified parameters
Use the calculated support vectors and SVM model to calculate a single detecting descriptor vector
Dry-run the newly trained custom HOG descriptor against training set and against camera images, if available
Platform: |
Size: 297984 |
Author: Walker |
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