Boosted HOG features and its application on object movement detection

Watada, J. and Zhang, H. and Melo, H. and Sun, D. and Vasant, P. (2018) Boosted HOG features and its application on object movement detection. Smart Innovation, Systems and Technologies, 81. pp. 340-348.

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Abstract

Nowadays, traffic accidents is universally decreasing due to many advanced safety vehicle systems. To prevent the occurrence of a traffic accident, the first function that a safety vehicle system should accomplish is the detection of the objects in traffic situation. This paper presents a popular method called boosted HOG features to detect the pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we use boosted HOG features to get an satisfying result. In detecting pedestrians part, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good performance in various kinds of background. On that basis, we create a new feature with boosting algorithm to obtain more accurate result. In detecting vehicles part, we use the shadow underneath vehicle as the feature, so we can utilize it to detect vehicles in daytime. The shadow is the important feature for vehicles in traffic scenes. The region under vehicle is usually darker than other objects or backgrounds and could be segmented by setting a threshold. © Springer International Publishing AG 2018.

Item Type: Article
Impact Factor: cited By 0; Conference of 13th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2017 ; Conference Date: 12 August 2017 Through 15 August 2017; Conference Code:195379
Uncontrolled Keywords: Accidents; Highway accidents; Multimedia signal processing; Object detection; Pedestrian safety; Signal processing; Traffic signals; Vehicles, Advanced safety vehicles; Boosting algorithm; Histograms of oriented gradients (HoG); Hog feature; Important features; Pedestrian detection; Traffic situations; Vehicle detection, Feature extraction
Departments / MOR / COE: Research Institutes > Institute for Autonomous Systems
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 01 Aug 2018 01:12
Last Modified: 20 Feb 2019 01:55
URI: http://scholars.utp.edu.my/id/eprint/21937

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