TY  - CONF
Y1  - 2016///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781479989966
A1  - Mukhtar, A.
A1  - Tang, T.B.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971633792&doi=10.1109%2fICSIPA.2015.7412234&partnerID=40&md5=507f4de72cb8c21ee3fa5e0d5f89c5d7
EP  - 456
AV  - none
N2  - In this paper, we present a motorcycle detection system in static images leading to its application in crash avoidance systems. Motorcycles are common mode of transport in ASEAN countries and contribute more road crashes than any other mode of transport. In our proposed system, motorbikes are detected based on the helmet and tyre color characteristics. This method involves the fusion of shape, color and corner features to hypothesize motorcycle locations in a video frame. The hypothesized locations are then classified using a support vector machine (SVM) classifier trained on histogram of oriented gradients (HOG) features of motorcycle database. The proposed technique was successfully designed and implemented on a standard PC. It was able to detect single and multiple motorcycles in videos with 96 detection rate. © 2015 IEEE.
N1  - cited By 16; Conference of 4th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2015 ; Conference Date: 19 October 2015 Through 21 October 2015; Conference Code:119504
TI  - Vision based motorcycle detection using HOG features
SP  - 452
ID  - scholars7185
KW  - Classification (of information); Computer vision; Highway accidents; Motorcycles; Pattern recognition; Pattern recognition systems; Signal detection; Support vector machines; Transportation
KW  -  Color characteristics; Corner feature; Crash avoidance; Detection rates; Histogram of oriented gradients (HOG); ITS applications; Mode of transport; Motorcycle detection
KW  -  Image processing
ER  -