Crowd abnormality detection in video sequences using supervised convolutional neural network

Hand gesture is one of the methods used in sign language for non-verbal communication. It is most commonly used by hearing & speech impaired people who have hearing or speech problems to communicate among themselves or with normal people. Developing sign language applications for hearing impaired people can be very important, as hearing & speech impaired people will be able to communicate easily with even those who don’t understand sign language. This project aims at taking the basic step in bridging the communication gap between normal people, deaf and dumb people using sign language. The main focus of this work is to create a vision based system to identify sign language gestures from the video sequences. The reason for choosing a system based on vision relates to the fact that it provides a simpler and more intuitive way of communication between a human and a computer. Video sequences contain both temporal as well as spatial features. In this project, two different models are used to train the temporal as well as spatial features. To train the model on the spatial features of the video sequences a deep Convolutional Neural Network. Convolutional Neural Network was trained on the frames obtained from the video sequences of train data. To train the model on the temporal features Recurrent Neural Network is used. The Trained Convolutional Neural Network model was used to make predictions for individual frames to obtain a sequence of predictions. Now this sequence of prediction outputs was given to the Recurrent Neural Network to train on the temporal features. Collectively both the trained models i.e. Convolutional Neural Network and Recurrent Neural Network will produce the text output of the respective gesture.

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Abnormality Detection from X-Ray Bone Images using DenseNet Convolutional Neural Network

International Journal of Current Research and Review ◽

10.31782/ijcrr.2021.131026 ◽

2021 ◽

Vol 13 (10) ◽

pp. 101-106

Author(s):

Shukla Abhilash ◽

Patel Atul

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Abnormality Detection ◽

X Ray

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Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2019.101792 ◽

2020 ◽

Vol 57 ◽

pp. 101792 ◽

Cited By ~ 2

Author(s):

M. Hajabdollahi ◽

R. Esfandiarpoor ◽

E. Sabeti ◽

N. Karimi ◽

S.M.R. Soroushmehr ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Medical Image ◽

Abnormality Detection ◽

Medical Image Diagnosis ◽

Image Diagnosis

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Analysis of Deep Rain Streaks Removal Convolutional Neural Network-Based Post-Processing Techniques in HEVC Encoder

Journal of Circuits System and Computers ◽

10.1142/s0218126621500201 ◽

2020 ◽

pp. 2150020

Author(s):

Thiyagarajan Jayaraman ◽

Gowri Shankar Chinnusamy

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Optimization Algorithm ◽

High Efficiency ◽

Convergence Time ◽

Video Sequences ◽

Bit Rate ◽

Post Processing ◽

Rate Reduction ◽

High Definition

This paper presents Deep Rain Streaks Removal Convolutional Neural Network (Derain SRCNN) based post-processing optimization algorithm for High-Efficiency Video Coder (HEVC). Earlier, the CNN-based denoising optimization algorithm faced overfitting issues and large convergence time when training the CNN for rain streaks affected High Definition (HD) video sequences. To address these problems, Deep rain streaks removal CNN-based post-processing block is introduced in HEVC encoder. Derain SRCNN architecture consists of a parallel two residual block layer and Dual Channel Rectification Linear Unit (DCReLU) activation function with various sizes of the convolutional layer. By reducing the validate error and training the error of CNN, the overfitting issue is solved. Also, convergence time is reduced using proper learning rate and kernel weight of optimization algorithm. The proposed network provides a higher bit rate reduction and higher convergence speed for corrupted high-definition video sequences. The experiment result shows that proposed DerainSRCNN-based post-processing filtering method achieves 6.8% and 4.1% -bit rate reduction for random access (RA) and low delay [Formula: see text] frame (LDP) configuration, respectively.

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