Object Detection and Recognition in Real-Time Video Streams

The ability to identify objects in real-time
video streams is among the most significant tasks in
computer vision and artificial intelligence. It is
commonly employed in video surveillance, business
monitoring, autonomous vehicles, robotics, and
smart megacity operations. The target of this
prototype is to characterize and recognize human,
vehicle, and other moving objects from live video
streams in high-speed and high-accuracy settings.
Traditional image processing methods do not work
for dealing with real-time detection because of
constraints in speed and accuracy. Thus, this design
uses deep learning-based object detection models
(similar to YOLO You Only Look Once) and
Convolutional Neural Networks (CNN) for the
performance of accurate real-time detection. The
system takes each frame of a video stream with the
corresponding objects, recognizes them, draws a
bounding box, and labels accordingly.
Our new system exhibits high detection accuracy,
low latency, and high performance. Through its
application and experimental findings, it appears
that deep learning-based methods have a significant
impact in enhancing object detection performance
relative to traditional methods. This design
contributes to the real-time intelligent monitoring
environment at work and the findings of smart
automation.