| dc.description.abstract | In real-time environment, the detection of motion-based object is detected
against challenging issues i.e. cluttered background. Practically, the desirable
results are hard to obtain due to challenging issues such as dynamic background,
illumination variation, floating or spouting water etc. The identification,
detection, tracking and recognition is a crucial area of research in the area of
computer vision that plays smart decision-based roles for intelligent video
surveillance system, intelligent transportation, indoor-outdoor, virtual reality,
medical diagnosis, robot vision navigation, law and enforcement, security,
border monitoring, and many more. With the advent of technological
advancements and availability of enormous data in the form of audios, videos,
text and other forms. Artificial Intelligence emerged as one of the most
significant technologies that simulates human intelligence through computers
and several algorithms. Computer vision is one of the prime domains that enables
to derive meaningful and crisp information from digital media. It includes several
sub domains such as facial recognition, pattern detection, image classification,
object detection and many others. Many vision-based applications such as traffic
controlling, action recognition, human behavior analysis, industrial inspection,
an intelligent surveillance are important issues for the researchers and
industrialists in the modern days.
This work presents the study of object detection in videos or continuous
sequence of frames captured by the static camera. The thesis will make the clear
image of comparison of object detection methods performed in prior researches.
The main focus is on the real time application areas of video surveillance and the
concerning challenges in the respective areas in near future. All the background
modeling techniques and their sub categories studied and implemented by
various authors is mentioned in literature. It also depicts the major challenging
issues available in real-time environments.
The significant aim of the proposed work is to develop an adaptive method
to compute the threshold during run-time and update it adaptively for each pixel
in testing phase. It classifies motion-oriented pixels from the scene for moving
object using background subtraction and enhanced using post processing. The
main objective is to focus and study the problematic video data captured through
camera sensor, to handle challenging issues available in real-time video scene
and to develop a background subtraction method and update the background
model adaptively for moving object detection.
Presently, the vehicle detection and tracking in intelligent transportation
system is a highly active area. The proposed work also resolves the above
problems and delivers solutions for the enhancement of the transportation system
and the automobile industry. So, the work investigates a method using Google’s
firebase platform (as a cloud service) and a background subtraction method for
moving vehicle detection and tracking in a foggy environment. Here, the
Google’s Firebox storage (Pyrebase API) is used for the storage of video that
provides authentication and storage services. The moving vehicle is detected
using the background subtraction method for considered video followed by post-
processing.
Detection and correctly tracking the moving objects in a video streaming are
still a challenging problem. Due to the high density of vehicles, it is difficult to
identify the correct objects on the roads. In this work, we have used a YOLO.v5
(You Only Look Once) algorithm to identify the different objects on road, such
as trucks, cars, trams, and vans which results that the proposed approach attains
improved results as compared to state-of-the-art approaches which is another
contribution of the thesis. | en_US |
