Description: 人体识别，运动跟踪监测，关节点检测
计算运动速率，检测健康状态-human identification, tracking and monitoring the movement, joint movement detection rate calculation, the state health Detection Platform: |
Size: 1482752 |
Author:柳建武 |
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Description: 鲁棒人脸跟踪系统学位论文；提出了一种两阶段的光照均衡的方法来消除单幅图像中的各种阴影包括模糊阴影，投射阴影等 根据快速的Adaboost 训练框架，本文提出了一个实时的鲁棒人脸检测算法 提出了一种基于在线实值boosting 的方法来处理人脸在跟踪过程中发生的外貌变化；实现了一个基于以上模块的完全自动化的人脸跟踪器-Robust Face Tracking System dissertation proposes a two-stage light-balanced approach to the elimination of single image in a variety of shadow including fuzzy shadow, projected shadow, etc. Under the fast-track the Adaboost training framework, this paper presents a real-time Robust Face Detection Algorithm Based on boosting online real ways to deal with people face in tracking occurred during the appearance of change the realization of a module based on the above fully automated face tracker Platform: |
Size: 1139712 |
Author:田卉 |
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Description: 基于SVM和AdaBoost的红外目标跟踪的论文，有需要的朋友可以下载-SVM and AdaBoost-based infrared target tracking of papers, there is a need to look at the friend can be downloaded Platform: |
Size: 313344 |
Author:李一 |
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Description: 基于Adaboost的快速人脸跟踪算法2004，该算法具有较强的自适应性-Adaboost-based fast algorithm for face tracking in 2004, the algorithm has strong adaptability Platform: |
Size: 338944 |
Author:薛用 |
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Description: Video tracking using Adaboost algorithm for auto-updating of the weak classifiers.
The object has to be defined by the user (no auto-recogntion). The tracker works for every type of object (no prior assumptions are taken to object size or form). Platform: |
Size: 14336 |
Author:yosko |
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Description: Adaboost 人脸检测的程序，实现静态图片人脸检测，摄像头实时人脸跟踪功能。速度较快，正脸检测率较高。-Adaboost face detection process, to achieve a static image, face detection, camera, real-time face tracking. Speed, face detection rate is higher. Platform: |
Size: 4692992 |
Author:yangch |
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Description: this adaboost based real time face detection and tracking system
i used a adaboost and camshift algorithm with opencv and vc++
The detεction efficiency of the method is not good for environment of dynanlic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance.-this is adaboost based real time face detection and tracking system
i used a adaboost and camshift algorithm with opencv and vc++
The detεction efficiency of the method is not good for environment of dynanlic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. Platform: |
Size: 19933184 |
Author:pattern |
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Description: The project tends to detect and track human face in a video via some basic algorithms. It uses adaboost for detection and camshift for tracking. It works well in stable environment. Platform: |
Size: 4370432 |
Author:linuszhao |
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Description: 基于adaboost和mean-shift视频中的人脸检测与跟踪识别-face detection and tracking recognition
of videoBased on adaboost and mean-shift Platform: |
Size: 5272576 |
Author:jianghui |
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Description: 基于AdaBoost和mean-shift视频中的人脸检测与跟踪识别-Based on AdaBoost and mean-shift video of face detection and tracking recognition Platform: |
Size: 18119680 |
Author:jianghui |
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Description: 基于AdaBoost和Kalman算法的人眼检测与跟踪-Eye detection and tracking based on AdaBoost and the Kalman algorithm Platform: |
Size: 470016 |
Author:linkpjc |
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Description: 跟踪人的眼睛，类似人脸识别算法中的adaboost，通过学习人眼模式，实现人眼检测跟踪。-Tracking people' s eyes, similar face recognition algorithm adaboost by learning human eye model, detecting and tracking the human eye. Platform: |
Size: 23378944 |
Author:cz |
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Description: Very recently tracking was approached using classification techniques such
as support vector machines. The object to be tracked is discriminated by a
classifier from the background. In a similar spirit we propose a novel on-line
AdaBoost feature selection algorithm for tracking. The distinct advantage of
our method is its capability of on-line training. This allows to adapt the classifier while tracking the object. Therefore appearance changes of the object
(e.g. out of plane rotations, illumination changes) are handled quite naturally.
Moreover, depending on the background the algorithm selects the most discriminating features for tracking resulting in stable tracking results. By using
fast computable features (e.g. Haar-like wavelets, orientation histograms, local binary patterns) the algorithm runs in real-time. We demonstrate the performance of the algorithm on several (publically available) video sequences. Platform: |
Size: 327680 |
Author:lili |
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Description: 视频跟踪是视频信息处理的一个重要研究方向。交通信息管理、公安刑事侦查过程中人的跟踪占有很大比例。基于人脸特征的视频跟踪主要研究视频信息中人脸特征的提取，人脸特征的对比算法，人的运动的分析方法，及跟踪方法，跟踪结果的表示。- criminal investigation,anti-terrorism and military installations. Consequently, to recognize person identity speedy and exactly in large-scale crowd has become an important way to protect the public social security, guarantee national harmony and reinforce pre-warning capability in public safety.This paper designs a real time automatic face detection, tracking and recognition system for video, which can detect, track faces and recognize human identity within the scope of video. The system consists of three parts, which are multi-face detection, multi-face tracking and identity recognition.
For face detection, this paper presents an face detection algorithm called Adaboost-ASM face detection algorithm, combines Adaboost face detection algorithm with the adaptive shape model (ASM), to solve the problem that Adaboost face detection algorithm recognizes the non-face region and complex region as a face region mistakenly.Finally, the Adaboost-ASM algorithm achieves the real time face Platform: |
Size: 641024 |
Author:bai |
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Description: This thesis proposes a novel algorithm for integrated face detection and face tracking
based on the synthesis of an adaptive particle filtering algorithm and an AdaBoost face detection
algorithm. A novel Adaptive Particle Filter (APF), based on a new sampling technique, is
proposed to obtain accurate estimation of the proposal distribution and the posterior distribution
for accurate tracking in video sequences. The proposed scheme, termed a Boosted Adaptive
Particle Filter (BAPF), combines the APF with the AdaBoost algorithm. The AdaBoost
algorithm is used to detect faces in input image frames, while the APF algorithm is designed to
track faces in video sequences. The proposed BAPF algorithm is employed for face detection,
face verification, and face tracking in video sequences. Experimental results confirm that the
proposed BAPF algorithm provides a means for robust face detection and accurate face tracking
under various tracking scenarios. Platform: |
Size: 1057792 |
Author:Trabelsi |
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Description: This paper presents an online feature selection algorithm
for video object tracking. Using the object and background
pixels from the previous frame as training samples, we model the
feature selection problem as finding a good subset of features to
better classify object from background in current frame. Platform: |
Size: 452608 |
Author:SAINATH1 |
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Search - adaboost tracking