@article{noauthororeditor,
  abstract = {Most of the existing image recognitions systems are based on physical parameters of the images whereas image processing methodologies relies on extraction of color, shape and edge features. Thus Transfer Learning is an efficient approach of solving classification problem with little amount of data. There are many deep learning algorithms but most tested one is AlexNet. It is well known Convolution Neural Network AlexNet CNN for recognition of images using deep learning. So for recognition and detection of the image we have proposed Deep Learning approach in this project which can analyse thousands of images which may take a lot for a human to do. Pretrained convolutional neural network i.e. AlexNet is trained by using the features such as textures, colors and shape. The model is trained on more than 1000 images and can classify images into categories which we have defined. The trained model is tested on various standard and own recorded datasets consist of rotational, translated and shifted images. Thus when a image is passed to the system it will apply AlexNet and return the results with a image category in which the image lies with high accuracy. Thus our project tends to reduce time and cost of image recognition systems using deep learning. Dr. Sachin K. Korde | Manoj J. Munda | Yogesh B. Chintamani | Yasir L. Pirjade | Akshay V. Gurme "Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31653.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31653/image-classification-using-deep-learning/dr-sachin-k-korde
},
  added-at = {2020-07-16T13:20:33.000+0200},
  author = {Gurme, Dr. Sachin K. Korde | Manoj J. Munda | Yogesh B. Chintamani | Yasir L. Pirjade | Akshay V.},
  biburl = {https://www.bibsonomy.org/bibtex/2b81d38516a8d07608e89f5c66060f915/ijtsrd},
  interhash = {552b14396c0436d2c2bc857a11a750a7},
  intrahash = {b81d38516a8d07608e89f5c66060f915},
  issn = {2456-6470},
  journal = {International Journal of Trend in Scientific Research and Development},
  keywords = {AlexNet Artificialintelligence CNN Deep Filter Image Intelligence Learning Median Recognition Transfer and},
  language = {English},
  month = {june},
  number = 4,
  pages = {1648-1650},
  timestamp = {2020-07-16T13:20:33.000+0200},
  title = {Image Classification using Deep Learning
},
  url = {https://www.ijtsrd.com/computer-science/artificial-intelligence/31653/image-classification-using-deep-learning/dr-sachin-k-korde},
  volume = 4,
  year = 2020
}

@article{noauthororeditor,
  abstract = {Closed circuit television systems CCTV play a vital role in evidence collection against crimes and criminals. The existing systems does not classify normal and abnormal events leading the police to become more reluctant to attend the crime scenes unless there was a visual verification, either by manned patrols or by electronic images from the surveillance cameras. The Proposed work is being used for surveillance, monitoring and classifications of weapons, live tracking and many more purposes. In this work, live surveillance videos is taken for monitoring and detecting the abnormal events based on real time image processing techniques. Operations of proposed project has three processing modules, first processing module is for object detection using Convolutional Neural Networks CNN and second processing module will handle the classification of weapons, monitoring and alarm operations will be carried out by the third processing module. CCTV will monitor circular area and it will automatically perform all operations and be controlled. Shape detection algorithms and object detection algorithms have been tested to find accuracy in detection and analysis the processing time before implementing in such environment and results provide optimal accuracy in matching weapons and objects type with name and shape in predefined database like ALEXNET. The proposed work drastically reduces the crime rate and it also provide a higher level security in certain areas and it will reduce the time required to catch the criminal. Bhagyalakshmi. P | Indhumathi. P | Lakshmi. R | Dr. Bhavadharini "Real Time Video Surveillance for Automated Weapon Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22791.pdf
},
  added-at = {2019-06-06T07:48:38.000+0200},
  author = {Bhavadharini, Bhagyalakshmi. P | Indhumathi. P | Lakshmi. R | Dr.},
  biburl = {https://www.bibsonomy.org/bibtex/2a005cf2b4f0dc538955677d20d217b66/ijtsrd},
  doi = {https://doi.org/10.31142/ijtsrd22791},
  interhash = {b9d2aaf9e6ac0b66ceb062ca6eb254b3},
  intrahash = {a005cf2b4f0dc538955677d20d217b66},
  issn = {2456-6470},
  journal = {International Journal of Trend in Scientific Research and Development},
  keywords = {ALEXNET CNN Video abnormal detection events object surveillance},
  language = {English},
  month = {March},
  number = 3,
  pages = {465-470},
  timestamp = {2019-06-06T07:48:38.000+0200},
  title = {Real Time Video Surveillance for Automated Weapon Detection
},
  url = {https://www.ijtsrd.com/computer-science/other/22791/real-time-video-surveillance-for-automated-weapon-detection/bhagyalakshmi-p},
  volume = 3,
  year = 2019
}