HOG, LBP and SVM based Traffic Density Estimation at Intersection
- URL: http://arxiv.org/abs/2005.01770v1
- Date: Mon, 4 May 2020 18:08:35 GMT
- Title: HOG, LBP and SVM based Traffic Density Estimation at Intersection
- Authors: Devashish Prasad, Kshitij Kapadni, Ayan Gadpal, Manish Visave, Kavita
Sultanpure
- Abstract summary: High amount of vehicular traffic creates traffic congestion, unwanted delays, pollution, money loss, health issues, accidents, emergency vehicle passage and traffic violations.
Traditional traffic management and control systems fail to tackle this problem.
There's a necessity of an optimized and sensible control system which would enhance the efficiency of traffic flow.
- Score: 4.199844472131922
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Increased amount of vehicular traffic on roads is a significant issue. High
amount of vehicular traffic creates traffic congestion, unwanted delays,
pollution, money loss, health issues, accidents, emergency vehicle passage and
traffic violations that ends up in the decline in productivity. In peak hours,
the issues become even worse. Traditional traffic management and control
systems fail to tackle this problem. Currently, the traffic lights at
intersections aren't adaptive and have fixed time delays. There's a necessity
of an optimized and sensible control system which would enhance the efficiency
of traffic flow. Smart traffic systems perform estimation of traffic density
and create the traffic lights modification consistent with the quantity of
traffic. We tend to propose an efficient way to estimate the traffic density on
intersection using image processing and machine learning techniques in real
time. The proposed methodology takes pictures of traffic at junction to
estimate the traffic density. We use Histogram of Oriented Gradients (HOG),
Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for
traffic density estimation. The strategy is computationally inexpensive and can
run efficiently on raspberry pi board. Code is released at
https://github.com/DevashishPrasad/Smart-Traffic-Junction.
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