Selecting relevant features for human motion recognition

Gehrig, Dirk; Schultz, Tanja

doi:10.1109/icpr.2008.4761290

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Currently, some studies are using ML models [27][28][29] and DL in human action recognition [5,30,31]. However, these studies only focus on developing RNN and Long Short Term Memory (LSTM) models to predict but do not care about the characteristics of the object and extraction feature methods …”

Section: Evaluation Of Resultsmentioning

confidence: 99%

Human action recognition method based on conformal geometric algebra and recurrent neural network

Nguyen

Pham

Hao

et al. 2020

ICS

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Deep Learning (DL) plays an important role in machine learning and artificial intelligence. DL is widely applied in many fields with high dimensional data, including natural language processing, image recognition. High dimensional data can lead to problems in machine learning such as overfitting, degradation of accuracy. To address these issues, some methods, such as Principal Components Analysis (PCA), principal component regression (PCR), Multi-class Linear Discriminant Analysis (MLDA), were proposed to reduce dimensions of the data and computational complexity simultaneously. The drawback of these methods is that they only work well on data distributed on the plane. In the case of the data distributed on the hyper-sphere, such as objects moving in space, the processing results are not so good as expected. In this paper, we propose the use of Conformal Geometric Algebra (CGA) to extract features and simultaneously reduce the dimensionality of a dataset for human activity recognition using Recurrent Neural Network (RNN). First, human activity data in a 3-dimensional coordinate system is pre-processed and normalized by calculating deviations from the mean coordinate. Next, the data is transformed to vectors in CGA space and its dimensions are reduced to return the feature vectors. Finally, we use the RNN model to train feature vectors. Empirical results performed on the CMU eight actions dataset show that the CGA combined with RNN gave the best test results of 92.5%.

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Section: Evaluation Of Resultsmentioning

confidence: 99%

Human action recognition method based on conformal geometric algebra and recurrent neural network

Nguyen

Pham

Hao

et al. 2020

ICS

View full text Add to dashboard Cite

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“…Of all the visual human action recognition networks constructed, Hidden Markov Models (HMM) are the most widely used (Yamato et al 1992;Campbell et al 1996;Starner et al 1998;Lee and Kim 1999;Galata et al 2001;Yu and Ballard 2002;Oliver et al 2000Oliver et al , 2002Antonakaki et al 2009;Gehrig and Schulz 2008;Gao et al 2009;Robertson and Reid 2006;Perez et al 2007;Shi et al 2008;Chakraborty et al 2008;Babu et al 2002;Hassan et al 2006). Hidden Markov Models keep a network of body poses related to each other and provided a way of learning parameters that best fit a set of training data with known classifications.…”

Section: Traditional Hidden Markov Modelsmentioning

confidence: 98%

Survey on classifying human actions through visual sensors

Rose¹,

Wagner

2011

Artif Intell Rev

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The ability to predict the intentions of people based solely on their visual actions is a skill only performed by humans and animals. This requires segmentation of items in the field of view, tracking of moving objects, identifying the importance of each object, determining the current role of each important object individually and in collaboration with other objects, relating these objects into a predefined scenario, assessing the selected scenario with the information retrieve, and finally adjusting the scenario to better fit the data. This is all accomplished with great accuracy in less than a few seconds. The intelligence of current computer algorithms has not reached this level of complexity with the accuracy and time constraints that humans and animals have, but there are several research efforts that are working towards this by identifying new algorithms for solving parts of this problem. This survey paper lists several of these efforts that rely mainly on understanding the image processing and classification of a limited number of actions. It divides the activities up into several groups and ends with a discussion of future needs.

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“…Many other papers also address simple scenario recognition based on probabilistic methods. In this area, most of the research is related to high level processing; some of them used rule-based methods [8][9][10][11][12]13], and others probabilistic methods [7,14,15].…”

Section:  Offline or Onlinementioning

confidence: 99%

Fuzzy Logic-Based Scenario Recognition from Video Sequences

Elbaşi

2013

Journal of Applied Research and Technology

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In recent years, video surveillance and monitoring have gained importance because of security and safety concerns. Banks, borders, airports, stores, and parking areas are the important application areas. There are two main parts in scenario recognition: Low level processing, including moving object detection and object tracking, and feature extraction. We have developed new features through this work which are RUD (relative upper density), RMD (relative middle density) and RLD (relative lower density), and we have used other features such as aspect ratio, width, height, and color of the object. High level processing, including event start-end point detection, activity detection for each frame and scenario recognition for sequence of images. This part is the focus of our research, and different pattern recognition and classification methods are implemented and experimental results are analyzed. We looked into several methods of classification which are decision tree, frequency domain classification, neural network-based classification, Bayes classifier, and pattern recognition methods, which are control charts, and hidden Markov models. The control chart approach, which is a decision methodology, gives more promising results than other methodologies. Overlapping between events is one of the problems, hence we applied fuzzy logic technique to solve this problem. After using this method the total accuracy increased from 95.6 to 97.2.

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Selecting relevant features for human motion recognition

Cited by 6 publications

References 11 publications

Human action recognition method based on conformal geometric algebra and recurrent neural network

Human action recognition method based on conformal geometric algebra and recurrent neural network

Survey on classifying human actions through visual sensors

Fuzzy Logic-Based Scenario Recognition from Video Sequences

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