Real-time Traffic Jam Detection from UAV Using Cascade Haar

AMER, S. ALHARTHI and SAAD, B. ALOTAIBI (2016) Real-time Traffic Jam Detection from UAV Using Cascade Haar. In: Fourth International Conference on Advances in Computing, Communication and Information Technology CCIT- 2016, 17 - 18 March, 2016, Birmingham City University, Birmingham, UNITED KINGDOM.

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Abstract

This paper contains an in depth work carried out in the area of real-time vehicle detection from an Unmanned Ariel Vehicle (UAV) to detect the traffic jam by counts the number of cars in the roadway. The opticalcamera located inside the UAV takes images from a particular angle and analysis is performed on the image to detect the vehicle (static or moving). To obtain the accurate and quick result, the analysis should be inground station. This paper presents a highly reliable technique to detect many different types of vehicles from images having variant backgrounds. This technique used Haar classifiers to perform this real-time application. The Haar classifier was trained with multiple features to accomplish the task. The task was successfully achieved with minimum false positive rate.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: UAV, Haar Classifier, vehicle Detection
Depositing User: Mr. John Steve
Date Deposited: 25 Mar 2019 12:13
Last Modified: 25 Mar 2019 12:13
URI: http://publications.theired.org/id/eprint/925

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