Memristive Threshold Logic Circuit Design of Fast Moving Object Detection

IEEE Transactions on Very Large Scale Integration (VLSI) Systems ◽

10.1109/tvlsi.2014.2359801 ◽

2015 ◽

Vol 23 (10) ◽

pp. 2337-2341 ◽

Author(s):

Akshay Kumar Maan ◽

Dinesh Sasi Kumar ◽

Sherin Sugathan ◽

Alex Pappachen James

Keyword(s):

Object Detection ◽

Circuit Design ◽

Fast Moving ◽

Logic Circuit ◽

Moving Object Detection ◽

Moving Object ◽

Threshold Logic

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Motion Compensation Based Fast Moving Object Detection in Dynamic Background

Communications in Computer and Information Science - Computer Vision ◽

10.1007/978-3-662-48570-5_24 ◽

2015 ◽

pp. 247-256 ◽

Author(s):

Wei Zhang ◽

Chenglong Li ◽

Aihua Zheng ◽

Jin Tang ◽

Bin Luo

Keyword(s):

Object Detection ◽

Motion Compensation ◽

Fast Moving ◽

Moving Object Detection ◽

Moving Object ◽

Dynamic Background

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A fast moving object detection method based on 2D laser scanner and infrared camera

10.1117/12.2202698 ◽

2015 ◽

Author(s):

Lina Zeng ◽

Meng Ding ◽

Tianci Zhang ◽

Zejun Sun

Keyword(s):

Object Detection ◽

Fast Moving ◽

Detection Method ◽

Laser Scanner ◽

Infrared Camera ◽

Moving Object Detection ◽

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Automatic Fast Moving Object Detection in Video of Surveillance System

IARJSET ◽

10.17148/iarjset.2017.4535 ◽

2017 ◽

Vol 4 (5) ◽

pp. 190-195 ◽

Author(s):

Pranali A. Pojage ◽

Ajay A. Gurjar

Keyword(s):

Object Detection ◽

Fast Moving ◽

Surveillance System ◽

Moving Object Detection ◽

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An adaptive algorithm for fast moving object detection from dynamic background based on cobebook

2016 4th International Conference on Robotics and Mechatronics (ICROM) ◽

10.1109/icrom.2016.7886808 ◽

2016 ◽

Author(s):

Mikael A. Mousse ◽

Cina Motamed ◽

Eugene C. Ezin

Keyword(s):

Object Detection ◽

Adaptive Algorithm ◽

Fast Moving ◽

Moving Object Detection ◽

Moving Object ◽

Dynamic Background

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Enhanced codebook algorithm for fast moving object detection from dynamic background using scene visual perception

Journal of Electronic Imaging ◽

10.1117/1.jei.25.6.061618 ◽

2016 ◽

Vol 25 (6) ◽

pp. 061618 ◽

Author(s):

Mikaël A. Mousse ◽

Cina Motamed ◽

Eugène C. Ezin

Keyword(s):

Visual Perception ◽

Object Detection ◽

Fast Moving ◽

Moving Object Detection ◽

Moving Object ◽

Dynamic Background

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Fast moving object detection using improved Gaussian mixture models

2014 International Conference on Audio, Language and Image Processing ◽

10.1109/icalip.2014.7009844 ◽

2014 ◽

Author(s):

Ye Song ◽

Na Fu ◽

Xiaoping Li ◽

Qiongxin Liu

Keyword(s):

Object Detection ◽

Mixture Models ◽

Fast Moving ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Moving Object Detection ◽

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Reconfigurable Threshold Logic Networks in FPGA for Moving Object Detection

2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) ◽

10.1109/mwscas.2019.8885258 ◽

2019 ◽

Author(s):

Alex Pappachen James ◽

Bhaskar Choubey ◽

Kizheppatt Vipin

Keyword(s):

Object Detection ◽

Moving Object Detection ◽

Moving Object ◽

Threshold Logic

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Semi-supervised neural network training method for fast-moving object detection

2018 14th Symposium on Neural Networks and Applications (NEUREL) ◽

10.1109/neurel.2018.8586986 ◽

2018 ◽

Author(s):

Igor Sevo

Keyword(s):

Neural Network ◽

Object Detection ◽

Fast Moving ◽

Moving Object Detection ◽

Moving Object ◽

Training Method ◽

Neural Network Training ◽

Network Training

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Robust and fast moving object detection in a non-stationary camera via foreground probability based sampling

2015 IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2015.7351738 ◽

2015 ◽

Author(s):

Kimin Yun ◽

Jin Young Choi

Keyword(s):

Object Detection ◽

Fast Moving ◽

Moving Object Detection ◽

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Fast moving object detection with non-stationary background

Multimedia Tools and Applications ◽

10.1007/s11042-012-1075-3 ◽

2012 ◽

Vol 67 (1) ◽

pp. 311-335 ◽

Author(s):

Jiman Kim ◽

Xiaofei Wang ◽

Hai Wang ◽

Chunsheng Zhu ◽

Daijin Kim

Keyword(s):

Object Detection ◽

Fast Moving ◽

Moving Object Detection ◽

Moving Object ◽

Stationary Background

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