ARTIFICIAL INTELLIGENCE
Sai Info solution provide the
Project Development & Training. We Develop Project for BE/ME/PHD. Artificial
intelligence (AI),
sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed
by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and
takes actions that maximize its chance of successfully achieving its
goals. Colloquially, the term "artificial intelligence" is
applied when a machine mimics "cognitive" functions that humans
associate with other human minds, such as "learning" and "problem
solving”. The scope of AI is disputed: as machines become increasingly capable,
tasks considered as requiring "intelligence" are often removed from
the definition, a phenomenon known as the AI effect, leading to the quip,
"AI is whatever hasn't been done yet." For
instance, optical character recognition is frequently excluded from
"artificial intelligence", having become a routine technology. Capabilities
generally classified as AI as of 2017 include
successfully understanding human speech, competing at the highest
level in strategic game systems (such as chess and Go), autonomous
cars, intelligent routing in content delivery network and military
simulations.
Today will see one example application of Artificial intelligence in Tracking in Low Frame Rate Video a Cascade Particle Filter
Tracking in Low Frame Rate Video a Cascade Particle Filter
ABSTRACT
Tracking
objects in low frame rate (LFR) video or with abrupt motion poses two main
difficulties which most conventional tracking methods can hardly handle: 1)
poor motion continuity and increased search space and 2) fast appearance
variation of target and more background clutter due to increased search space.
In this paper, we address the problem from a view which integrates conventional
tracking and detection and present a temporal probabilistic combination of
discriminative observers of different life spans. Each observer is learned from
different ranges of samples, with different subsets of features, to achieve
varying levels of discriminative power at varying costs. An efficient fusion
and temporal inference is then done by a cascade particle filter which consists
of multiple stages of importance sampling. Experiments show significantly
improved accuracy of the proposed approach in comparison with existing tracking
methods, under the condition of LFR data and abrupt motion of both target and
camera.
INTRODUCTION
Tracking in low frame rate (LFR) video is a practical requirement
of many of today’s real-time applications such as in micro embedded systems and
visual surveillance. The reason is various: hardware costs, LFR data source, online
processing speed which upper-bounds the frame rate, etc. Moreover, for a tracking
system, LFR condition is equivalent to abrupt motion, which is often encountered
but hard to cope with. Although the body of literature regarding tracking is huge,
most existing approaches (except a few categories) cannot be readily applied to
LFR tracking problems, either because of the slow speed or the vulnerability to
motion and appearance discontinuity caused by LFR data.
Figure 1. Tracking 4 consecutive frames in a 5pfs video
The key notion of our solution is that detection and tracking can be integrated to overcome this difficulty. As two extremes, conventional tracking facilitates itself with every possible assumption of temporal continuity, while detection aims at the universal description or discrimination of the target from the others. In LFR tracking, the continuity of tar-get is often too weak for conventional tracking (Figure 1);meanwhile, applying reliable detection over a large search space is often unaffordable, neither is it capable of identifying target through frames due to neglect of context.
REFERENCE ARTICLES
- Design of Variable Vehicle Handling Characteristics Using Four-Wheel Steer-by-Wire
- Decentralized RFID Coverage Algorithms with Applications for the Reader Collisions Avoidance Problem
- An Efficient Privacy-Preserving Ranked Keyword Search Method
- Next Generation Data Classification and Linkage
- Anomaly Detection via Online Oversampling Principal Component Analysis
If anyone is interested for doing Research in above subject for BTech/MTech/PHD Engineering project work
Kindly Contact Below
Contact Details:
Santosh Gore Sir
Ph:09096813348 / 8446081043 / 0253-6644344
Email: sai.info2009@gmail.com
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