1,386 research outputs found
Complex Human Action Recognition in Live Videos Using Hybrid FR-DL Method
Automated human action recognition is one of the most attractive and
practical research fields in computer vision, in spite of its high
computational costs. In such systems, the human action labelling is based on
the appearance and patterns of the motions in the video sequences; however, the
conventional methodologies and classic neural networks cannot use temporal
information for action recognition prediction in the upcoming frames in a video
sequence. On the other hand, the computational cost of the preprocessing stage
is high. In this paper, we address challenges of the preprocessing phase, by an
automated selection of representative frames among the input sequences.
Furthermore, we extract the key features of the representative frame rather
than the entire features. We propose a hybrid technique using background
subtraction and HOG, followed by application of a deep neural network and
skeletal modelling method. The combination of a CNN and the LSTM recursive
network is considered for feature selection and maintaining the previous
information, and finally, a Softmax-KNN classifier is used for labelling human
activities. We name our model as Feature Reduction & Deep Learning based action
recognition method, or FR-DL in short. To evaluate the proposed method, we use
the UCF dataset for the benchmarking which is widely-used among researchers in
action recognition research. The dataset includes 101 complicated activities in
the wild. Experimental results show a significant improvement in terms of
accuracy and speed in comparison with six state-of-the-art articles
Human object annotation for surveillance video forensics
A system that can automatically annotate surveillance video in a manner useful for locating a person with a given description of clothing is presented. Each human is annotated based on two appearance features: primary colors of clothes and the presence of text/logos on clothes. The annotation occurs after a robust foreground extraction stage employing a modified Gaussian mixture model-based approach. The proposed pipeline consists of a preprocessing stage where color appearance of an image is improved using a color constancy algorithm. In order to annotate color information for human clothes, we use the color histogram feature in HSV space and find local maxima to extract dominant colors for different parts of a segmented human object. To detect text/logos on clothes, we begin with the extraction of connected components of enhanced horizontal, vertical, and diagonal edges in the frames. These candidate regions are classified as text or nontext on the basis of their local energy-based shape histogram features. Further, to detect humans, a novel technique has been proposed that uses contourlet transform-based local binary pattern (CLBP) features. In the proposed method, we extract the uniform direction invariant LBP feature descriptor for contourlet transformed high-pass subimages from vertical and diagonal directional bands. In the final stage, extracted CLBP descriptors are classified by a trained support vector machine. Experimental results illustrate the superiority of our method on large-scale surveillance video data
Comprehensive review of vision-based fall detection systems
Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers
Character Recognition
Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field
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