Description: 语音端点检测是语音识别中至关重要的技术。无论军用还是民用，语音端点检测都有着广泛的应用。在低信噪比的环境中进行精确的端点检测比较困难，尤其是在无声段或者发音前后-voice activity detection is critical speech recognition technologies. Whether military or civilian, voice endpoint detection have broad application. Low signal-to-noise ratio in the environment for accurate endpoint detection more difficult, especially in or pronunciation of the silent before and after Platform: |
Size: 531456 |
Author:李一 |
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Description: Activity recognition program using Motion History Image s 7 Hu moments. The program compare the Mahalanobis distance of the Hu moments of the video input with the reference and find out the activities. Platform: |
Size: 246784 |
Author:Illidanstorm |
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Description: Gait extraction toolbox which is used in matlab abd very useful for human activity recognition Platform: |
Size: 7682048 |
Author:serhat |
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Description: 步态识别算法代码，对多种识别算法予以实现，以及一些论文上提到方法的测试程序。-Gait recognition algorithm code for a variety of recognition algorithms to be realized, as well as some papers on the method of testing procedures mentioned. Platform: |
Size: 13312 |
Author:ray |
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Description: 语音活动识别vad算法，另外有算法的相关文献。应该会有帮助-Voice Activity recognition vad algorithm, another algorithm of the relevant literature. Should be helpful Platform: |
Size: 3130368 |
Author:朱鹏 |
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Description: 手势识别函数库,基于隐马尔可夫模型。JAVA实现。-The Gesture and Activity Recognition Toolit (GART) is
a user interface toolkit designed to enable the development of gesturebased
applications. GART provides an abstraction to machine learning
algorithms suitable for modeling and recognizing di® erent types of
gestures. The toolkit also provides support for the data collection and
the training process. In this paper, we present GART and its machine
learning abstractions. Furthermore, we detail the components of the
toolkit and present two example gesture recognition applications. Platform: |
Size: 948224 |
Author:李明 |
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Description: 人体运动视觉分析主要包括运动目标检测、 运动 目标分类 、 人体运动跟踪、 人体行为识别与描述四个环
节 ， 在多领域具有广阔的应用前景． 本文从上述四个方面综述了人体运动分析的研究现状， 对人体运动分析的热点
难点进行讨论 ， 对可能的发展方向进行阐述和展望．-Visual analysis includes moving object detection,moving object classfication,human tracking and activity recognition and description. It has broad application prospects in many fields, such as smart vision surveillance,visual reality, intelligent human-computer interface,video compression and computer-aided clinical diagnosis. Acomprehensive survey on vision-based human motion analysis is presented from the above four aspects,and the challenges and future directions are discussed. Platform: |
Size: 700416 |
Author:有来有去 |
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Description: Centinela is a human activity recognition system based on data from an accelerometer and sensor unit. This is powerpoint presentation on a pervasive compurting application.-Centinela is a human activity recognition system based on data from an accelerometer and sensor unit. This is powerpoint presentation on a pervasive compurting application. Platform: |
Size: 718848 |
Author:shruthi rajan |
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Description: Object detection and tracking are important in many computer vision applications including activity recognition, automotive saf ety,
and surveillance. In this example, you w ill develop a simple f ace tracking system by dividing the tracking problem into three
separate problems:
1. Detect a f ace to track
2. Identif y f acial f eatures to track
3. Track the f ace Platform: |
Size: 887808 |
Author:Nurita |
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Description: 外国人写的行为动作识别，运用加速度传感器，具有较高识别率，又实用价值-Activity recognition has recently gained attention as a research
topic because of the increasing availability of accelerometers in
consumer products, like cell phones, and because of the many
potential applications. Platform: |
Size: 428032 |
Author:shenaimin |
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Description: A feature selection based framework for human activity recognition using wearable multimodal sensors Platform: |
Size: 208896 |
Author:by |
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Description: 功能介绍
• 通过拍照界面，指导用户拍出合格证件图像。
• 采用文字识别（OCR）技术，自动识别银行卡信息（如卡号,卡所属银行等）.
• 通过调用 识别功能Activity，实现其他应用程序接口调用。
• 识别银行卡种类，主要是国内外20多家银行的印刷字体（平面黑色字体卡类）和凸面字体银行卡(包括字符间距类型为6-13、4-4-4-4-3、4-4-4-4等类型)。
系统功能
• 对原图像进行倾斜矫正、抠图银行卡区域。
• 通过形态学和目前检测思路。对字符进行区域定位和单个字符分割。
• 对单个字符进行识别
-Features • The camera interface to guide users to shoot images qualification documents. • using character recognition (OCR) technology to automatically identify the bank card information (such as the card number, card-owned banks). • Recognition function call interface by calling Activity, implement other applications. • Identify the types of bank cards, mainly typography (flat black font cards) and convex font bank cards more than 20 domestic and foreign banks (including the character spacing type 6-13,4-4-4-4-3,4-4-4-4 other types). System functions • the original image tilt correction, matting bank card area. • currently detected by morphological and ideas. Regional location and character of individual character segmentation. • A single character recognition Platform: |
Size: 11414528 |
Author:笨笨 |
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Description: In this programe, a four-dimensional spatiotemporal shape context
descriptor is introduced and used for human activity recognition in video Platform: |
Size: 238592 |
Author:doski |
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Description: This a knn test file, used for human activity recognition, the knn is based on matlab knn algorithm.-This is a knn test file, used for human activity recognition, the knn is based on matlab knn algorithm. Platform: |
Size: 2343936 |
Author:chiang |
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Description: 一种HMM可以呈现为最简单的动态贝叶斯网络。隐马尔可夫模型背后的数学是由LEBaum和他的同事开发的。它与早期由RuslanL.Stratonovich提出的最优非线性滤波问题息息相关，他是第一个提出前后过程这个概念的。
在简单的马尔可夫模型（如马尔可夫链），所述状态是直接可见的观察者，因此状态转移概率是唯一的参数。在隐马尔可夫模型中，状态是不直接可见的，但输出依赖于该状态下，是可见的。每个状态通过可能的输出记号有了可能的概率分布。因此，通过一个HMM产生标记序列提供了有关状态的一些序列的信息。注意，“隐藏”指的是，该模型经其传递的状态序列，而不是模型的参数；即使这些参数是精确已知的，我们仍把该模型称为一个“隐藏”的马尔可夫模型。隐马尔可夫模型以它在时间上的模式识别所知，如语音，手写，手势识别，词类的标记，乐谱，局部放电和生物信息学应用。
隐马尔可夫模型可以被认为是一个概括的混合模型中的隐藏变量（或变量），它控制的混合成分被选择为每个观察，通过马尔可夫过程而不是相互独立相关。最近，隐马尔可夫模型已推广到两两马尔可夫模型和三重态马尔可夫模型，允许更复杂的数据结构的考虑和非平稳数据建模。-The HMM is a statistical approach in which the underlying model is a stochastic Markovian
process that is not observable (i.e., hidden) whic h can be observed through other processes that
produce the sequence of observed (emitted) features. In our HMM we let the hidden nodes represent
activities. The observable nodes re present combinations of the features described earlier. The
probabilistic relationships between hidden nodes and observable nodes and the probabilistic transition
between hidden nodes are estimated by the relative fr equency with which these relationships occur in
the sample data. An example HMM for three of the activities is shown in Figure 3. Given an input
sequence of sensor events, our algorithm finds the mo st likely sequence of hidden states, or activities,
which could have generated the observed event sequence. We use the Viterbi algorithm to
identify this sequence of hidden states. Platform: |
Size: 37888 |
Author:guolei |
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Description: 基于多层神经网络的人类活动识别，智能家居领域的一项重大突破。-Activity recognition has received increasing attention from
the machine learning community. Of particular interest is the ability to
recognize activities in real time streaming data, but this presents a
number of challenges not faced by traditional offline approaches. Among
these challenges is handling the large amount of data that does not
belong to a predefined class. In this paper, we describe a method by
which activity discovery can be used to identify behavioral patterns in
observational data. Discovering patterns in the data that does not belong
to a predefined class aids in understanding this data and segmenting it
into learnable classes. Platform: |
Size: 4117504 |
Author:guolei |
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Description: 基于加速度传感器的动作识别，文章内容包括数据的采集和处理。-Action recognition based on the acceleration sensor, the article includes the acquisition and processing of data. Platform: |
Size: 155648 |
Author:sunhao |
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Description: 与经典的方法相比，使用具有长时间记忆细胞的递归神经网络(RNN)不需要或几乎不需要特征工程。数据可以直接输入到神经网络中，神经网络就像一个黑匣子，可以正确地对问题进行建模。其他研究在活动识别数据集上可以使用大量的特征工程，这是一种与经典数据科学技术相结合的信号处理方法。这里的方法在数据预处理的数量方面非常简单(Compared with the classical methods, the recursive neural network (RNN) with long-term memory cells does not need or almost need feature engineering. Data can be directly input into the neural network, which acts as a black box and can correctly model the problem. Other research can use a lot of Feature Engineering on activity recognition data sets, which is a signal processing method combined with classical data science and technology. The method here is very simple in terms of the number of data preprocessing) Platform: |
Size: 266240 |
Author:一片真心 |
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