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Recognition of human movements is very useful for several applications, such as smart rooms, interactive virtual reality systems, human detection and environment modeling. The objective of this work focuses on the detection and classification of falls based on variations in human silhouette shape, a key challenge in computer vision. Falls are a major health concern, specifically for the elderly. In...
Real-time head pose recognition is an important function of Advanced Driver Assistance System that need to consider the driver's intention. The majority of early head pose recognition techniques utilize multi-axis sensors or 2D camera images to estimate the head pose in 3D space. When running the head pose recognition systems, the driver has to carry a device with multi-axis sensors or the system...
This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of...
This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass...
Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches...
This study proposes a symmetry-based forward vehicle detection and collision warning system (FCW) on smartphone. The proposed system identifies forward vehicle by shadow with vehicular symmetry. Through Bayes classifier tracking approach, it can reduce the error detection of image processing. Shadow detection with symmetry-based approach could improve the robustness of identifying forward vehicle...
An image based distance self-calibrating method for automotive electronic system is proposed to ease the difficulty in setup procedure and offset the possible changes caused by moving the camera that used to capture image for distance measurement. By process the information from vertical edge of dashed road line, the lookup table is established to translate the pixel distance to real distance. The...
Based on lane-marking tracking with fuzzy adjustable vanishing point mechanism, this paper presents robustness forward vehicle detection system. Compared to most of the detection systems with a large curvature of road trend, which are not effective for the routes to detection and marking. Therefore, follow the current image frame, the proposed system calculate the error between lane detection point...
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