| VR21_P01 |
Chinmay Tayade (IIT2018138), Inayat Baig (IIT2018165), Madhu (IIT2018068), Gurutej (IIT2018193) |
Number Plate Detection and Identification of Vehicles |
Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to
many practical applications. However, many of the current solutions are still not robust in
real-world situations, commonly depending on many constraints.
The vehicles have their own unique Number plate. Therefore we can identify the vehicle and
the owner details of any particular vehicle.
Therefore, we propose a model based on YOLO (a Deep learning based object detection
architecture) and OCR which detects the vehicles and their number plates and identifies the
other details of the vehicle. |
| VR21_P02 |
Vikash (IIT2018110), Hitesh Kumar (IIT2018160), Shubham S (IIT2018200), M J Akhil Naik (IIT2018143), Nilang (IIT2018147) |
Moving Object Detection and Tracking with ISR |
Object detection and tracking are the critical
steps of computer imaginative and prescient algorithms.
The sturdy object detection is the task due to variations
inside the scenes. any other largest undertaking is to song
the item within the occlusion conditions. In this method,
shifting gadgets detection using TensorFlow item detection
API, and CNN based object detection algorithm is used
for strong object detection which takes the vicinity of the
detected object as enter. The proposed method is able to
detect and tune the object in one of a kind illumination
and occlusion from mpeg video layout. |
| VR21_P03 |
Raushan Raj (IIT2018031), Bindu (IIT2018105), Ayushi Gupta (IIT2018118), Sanjana (IIT2018120) |
Masked Face Recognition using Neural Networks |
In this technological era, artificial intelligence has become
the new powerhouse of data analysis. With the advent
of various machine learning and computer vision algorithms,
their application in data analysis has become a
general trend. However, the application of deep neural networks
to various tasks of analyzing masked face data and
studying the performance of these models has yet to be explored
to a great extent. So in this article we have proposed
a model, which has been trained as such when you feed an
image as input, our model will be in a position to recognize
that and will print the name of the person. Our proposed
models achieved fairly high precision with a low cross entropy
rate. |
| VR21_P04 |
Atul Kumar (IIT2018030), Aman Raj Patwa (IIT2018038), Anshul Ahirwar (IIT2018099) |
Table Detection and Content Extraction from PDF Document Images |
Detecting and recognizing objects in unstructured
environments is a difficult task in computer vision research.
Table detection in document images is a challenge because the
tables are diverse in size and complexity. This work provides
an effective way to not only detect Tables but also using OCR
on the detected text to extract the required content of the table. |
| VR21_P05 |
Kisalaya Kishore (IIT2018079), Milan Bhuva (IIT2018176), Mohammed Aadil (IIT2018179), Ankit Rauniyar (IIT2018202) |
Interactive Indoor Scene Description to Aid in Navigation for Visually Impaired Individuals using Deep Learning |
This work introduces a methodology to help visually
impaired person to avoid obstacles in an indoor environment. We
use DepthNet-MiDaS large model to get the depth map of the
scene, and then use sparse optical flow in parallel to predict the
path of interested objects.
This is done in order to recognise objects that might cross
paths and are of potential danger to the user. |
| VR21_P06 |
Naukesh Goyal (IIT2018092), N.Lokesh Naik (IIT2018104), Nikhil Kumar (IIT2018152), Vishal Muwal (IIT2018153) |
Rating Content Based on Real-Time User Expressions |
We are making a custom facial emotion detection system
with a validation accuracy of 68.32% on the FER2013
dataset along with a proof of concept web app that captures
your facial emotion data while you watch any content in a
temporal manner with dynamic granularity so that we can
generate better input data for advanced user content
recommendation algorithms. There are a few
state-of-the-art models with accuracy as high as 71-72
percent but our model is quite a bit faster than those models
and it can be used in real-time on mobile devices as the
trained model will result in fewer parameters. |
| VR21_P07 |
Hrutvik Kailas Nagrale (IIT2018088), Aastha Kumari (IIT2018091), Ravi Kumar Sharma (IIT2018108), Ratan Kumar Mandal (IIT2018136) |
Real Time Indian Sign Language Recognition |
Sign language is one of the oldest and most natural
forms of language for communication, but since most people do
not know sign language and interpreters are very difficult to find
in day to day conversations, we have come up with a real time
method for finger spelling based Indian sign language. In our
method, the hand is first passed through a median blur filter and
canny edge detection algorithm is applied to the filtered image,
then feature extraction using SURF is performed on the result
and further model of visual words are obtained after clustering,
and then svm classifier is trained on histogram computed through
these visual words to generate our model. The method used
provides 99% accuracy for the 26 letters of the alphabet and 1
to 9 numbers. |
| VR21_P08 |
Jaya Meena (IIT2018029), Suryasen Singh (IIT2018069), Rahul Yadav (IIT2018071), Vineet Kumar (IIT2018096) |
Explicit Image Detection |
This paper explores the task of classification of images as an explicit
or non-explicit. There are various ways to deal with binary classification
of images varies from simple CNN architecture to more
sophisticated model as VGG, ResNet, etc. After several hit and trial,
We settled with ResNet architecture for our classification task as it
had the higher accuracy among other tried models. The dense layer
and the output layer of the model fine tuning has been found by hit
and trial by recursive testing of the designed model architecture.
In this paper we have proposed an approach for detecting images
which are considered to be explicit or Not Safe for Work contents
and prevent the consumption of such explicit content. The proposed
model is a deep learning model which is based upon a residual network.
The model returns a numerical value which is a measure
of explicitness of the input media content. The numerical value is
compared to a further defined threshold to categorize the content
into explicit or non-explicit content. |
| VR21_P09 |
Rithik Seth (IIT2018032), Hardik Kumawat (IIT2018034), Aman Joshi (IIT2018042), Milind Khatri (IIT2018082) |
Vision Assistant for Visually Impaired Individuals |
Artificial Intelligence has been touted as the next
big thing that is capable of altering the current landscape
of the technological domain. Through the use of Artificial
Intelligence and Machine Learning, pioneering works have
been undertaken in the area of Vision and Object Detection.
In this work, we undertake the analysis of a Vision Assistant
Application for guiding the visually-impaired individuals.
With recent break- through in the computer vision and
supervised learning models, the problem at hand has been
reduced significantly to the point where new models are easier
to build and implement over the already existing models.
Now, different Object Detection models exist which provides
object tracking and detection with great accuracy. These
techniques have been well used in automating the detection
task in different areas. Some of the newfound detection
approaches such as YOLO (You Only Look Once), SSD (Single
Shot Detector) and R-CNNs have proved to be consistent and
pretty accurate in Real-Time Object Detection. We are going
to have a brief look at these techniques in order to find a
good base model for implementing our ‘Vision Assistant’. |
| VR21_P10 |
Rahul Reddy Muppidi (IIT2018103), G Shashank (IIT2018106), A Prathyush (IIT2018124), A Rahul Naidu (IIT2018192) |
Building Detection from Aerial Images |
In this project, we do building extraction from high resolution
aerial images. There are many real life applications
like government decision making, civil defense operations,
police and Geographic Information Systems. But the complexity
of building extraction methods from the very high
resolution methods is still a great challenge. Hence using
deep learning methods has decreased the complexity and
increased the accuracy by great extent. In this project, a
framework based on CNN and edge detection is proposed.
The method is called Mask R-CNN. This method is applied
in Satellite image building extraction. Our method consists
of three parts. Quickly identifying buildings in disaster areas
plays a major role in disaster assessment. This paper
combines traditional digital image processing methods and
CNNs. Mask R-CNN improves detection accuracy. As the
complexity is also decreased, the computational time is also
reduced by a great extent. |
| VR21_P11 |
Ashwani Rai (IIT2018006), Sanjay Swami (IIT2018014), Sunidhi Kashyap (IIT2018016), Abhishek Mishra (IIT2018026) |
Deep CNN Model for Smoke Detection in Normal And Foggy Environment |
Smoke detection is very important especially in foggy environment so this is our problem
statement “Design a model which can take care of the increase in fire accidents in smart
cities”. |
| VR21_P12 |
Kshitij K. Gautam(IIT2018037), Rahul Thalor (IIT2018070), Divyansh Bhorvanshi (IIT2018072), Sourabh Thakur (IIT2018101) |
Hand Gesture Recognition: Contactless ATM |
As we know, in a pandemic a lot of people fear going out and are scared of touching anything
in public places. But there are some places like ATMs which can’t be accessed without
touching. So we came up with a solution to build an Contactless ATM so that people can
avoid contact in the ATMs too. |
| VR21_P13 |
Jishan Singh (IIT2018111), Prabal Tikeriha (IIT2018140), Fahad Ali (IIT2018148), Bhavya Jain (IIT2018151) |
Disguised Face Age Estimation |
With time, Age estimation models
have improved dramatically but what poses
challenges to these is human disguises like
mask, beard, mustache, etc. In this work, we develop the solution to
estimate the age of the disguised person. |
| VR21_P14 |
Sagar Kumar (IIT2018154), Kartik Nema (IIT2018156), Bhupendra (IIT2018163), Prakhar Srivastava (IIT2018172) |
Text to Image Synthesis |
In this work we discuss a solution to the problem of text
to image synthesis by making use of GANs (Generative Adversarial
Networks), we start our discussion with a brief introduction
of this field, it’s usefulness, challenges involved
etc. Then we discuss in depth about GANs, followed by our
own proposed methodology. |
