A survey on fall detection: Principles and approaches

Pattern recognition

"Graefe's Archive" is a distinguished international journal that presents original clinical reports and clinically relevant experimental studies. Founded in 1854 by Albrecht von Graefe to serve as a source of useful clinical information and a stimulus for discussion, the journal has published articles by leading ophthalmologists and vision research scientists for more than a century. With peer review by an international Editorial Board and prompt English-language publication, "Graefe's Archive" provides rapid dissemination of clinical and clinically related experimental information.

Graefe's archive for clinical and experimental ophthalmology

Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.

WiFall: Device-Free Fall Detection by Wireless Networks

Traditional and recent approaches in background modeling for foreground detection: An overview

In this paper, we present a new SWT based algorithm for electronic digital image stabilization application. In our proposed algorithm, we first estimate the translational motion by projecting the vertical and horizontal details of stationary wavelet decomposition in level 2. These parameters are used as initial estimation of motion's parameters. Then, we use several feature points and a gradient based algorithm to estimate the precise motion parameters. We compared our algorithm with two other algorithms, i.e. Moriomoto and Guestrin's stabilization algorithms. Experimental results indicate that our proposed algorithm has superior performance than these two.

An electronic digital image stabilizer based on stationary wavelet transform (SWT)

In this paper, a fast highlight detection and scoring method is proposed using an ondemand feature extraction and a fuzzy inference system. The proposed method partitions video to highlights and analyzes their content using an on-demand feature extraction approach. Then, a score is assigned to each highlight using a Fuzzy Inference System (FIS) according to the analyzed content. The assigned score determines importance of the events occurred in the highlight. This method is useful for flexible video summarization. The proposed method for on-demand feature extraction is a heuristic model of attention control that reduces computational complexity of the algorithm greatly. Additionally, FIS offers a simple and robust solution for content analysis. Experimental results illustrate that the proposed method is fast and processes about 130 frames per second on a personal computer. In addition, objective and subjective evaluations show that the proposed method generates high quality results for highlight detection, scoring and video summarization.

/pdf/fast-highlight-detection-and-scoring-for-broadcast-soccer-13ohs48edm.pdf

Fast Highlight Detection and Scoring for Broadcast Soccer Video Summarization using On-Demand Feature Extraction and Fuzzy Inference

In this paper we propose two new matching criteria for template matching. The performance evaluation of these two criteria is applied to an electronic digital image stabilizer (EDIS) application. These two criteria are based on bit-plane matching (BPM) criterion, where four decimated bit-planes are used in our criteria. These criteria can be realized using only Boolean functions; hence they can be realized very simply in any digital systems. We compared our criteria with other known criteria by employing real image sequences.

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Weighted multiple bit-plane matching, a simple and efficient matching criterion for electronic digital image stabilizer application

http://profdoc.um.ac.ir/articles/a/1079962.pdf

Retinal image assessment using bi-level adaptive morphological component analysis.

In different areas of Biometrics, recognition by iris images in nowadays has been taken into consideration by researchers as one of the common methods of identification like passwords, credit cards or keys. Iris recognition a novel biometric technology has great advantages such as variability, stability and security. Although the area of the iris is small it has enormous pattern variability which makes it unique for every one and hence leads to high reliability. In this paper we propose a new feature extraction method for iris recognition based on contourlet transform. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales so for reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. At last, the feature vector is created by using Co-occurrence matrix properties. For analyzing the desired performance of our proposed method, we use the CASIA dataset, which is comprised of 108 classes with 7 images in each class and each class represented a person. And finally we use SVM and KNN classifier for approximating the amount of people identification in our proposed system. Experimental results show that the proposed increase the classification accuracy and also the iris feature vector length is much smaller versus the other methods.

/pdf/a-novel-method-using-contourlet-to-extract-features-for-iris-3yilkj48fc.pdf

Hamid Reza Pourreza

Papers

An electronic digital image stabilizer based on stationary wavelet transform (SWT)

Fast Highlight Detection and Scoring for Broadcast Soccer Video Summarization using On-Demand Feature Extraction and Fuzzy Inference

Weighted multiple bit-plane matching, a simple and efficient matching criterion for electronic digital image stabilizer application

Retinal image assessment using bi-level adaptive morphological component analysis.

A novel method using contourlet to extract features for iris recognition system