What is it about?
A robust and reliable vehicle detection algorithm is proposed in this paper. The proposed method makes use of one feature in detecting and tracking of vehicles. Only the corner points of the vehicle are calculated to detect the vehicles. The proposed system is simple and time effective. Aerial surveillance has the advantage of a wide field of view. It provides a suitable view continuously to check the traffic density of the vehicles.
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Why is it important?
The background subtraction method is best suited for images captured by static camera, and it cannot work under certain conditions such as camera shaking, complex background, etc. This difficulty needs to be addressed in order to improve the accuracy of the traffic surveillance system. In the proposed algorithm, the objective is to detect vehicles in static as well as in moving camera images captured under different environmental conditions and complex backgrounds. The main contributions of the paper lie in localizing the vehicles based on the corner points and clustering these points.
Perspectives
This article is one of most important works of mine and would shift the researchers towards this technique because the algorithm is simple, effective and reliable. It has been tested on different videos of various climatic conditions like rainy, foggy, snow and sunny day with high and low resolution, were considered has an input to the system, not only for the different types of climates but also for the moving cameras as well .
Dr Mallikarjun Anandhalli
Central University of Karnataka
Read the Original
This page is a summary of: An Approach to Detect Vehicles in Multiple Climatic Conditions Using the Corner Point Approach, Journal of Intelligent Systems, July 2018, De Gruyter,
DOI: 10.1515/jisys-2016-0073.
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