Moving vehicle detection based on dense SIFT and Extreme Learning Machine for visual surveillance

Detecting vehicles in video is a challenging problem owing to the motion of vehicles, the camera and the background and to variations of speed. This paper proposes a classifier based supervised method to detect moving vehicles from a moving camera. Dense scale invariant feature transform (dense SIFT...

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Bibliographic Details
Published in2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) pp. 1614 - 1618
Main Authors Yuxiang Cai, Lin Li, Shilong Ni, Junyu Lv, Weibo Zeng, Yu Yuanlong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2015
Subjects
Cameras
dense scale invariant feature transform
extreme learning machine
Feature extraction
Histograms
low computational cost
Moving vehicle detection
Real-time systems
Training
Vehicle detection
Vehicles
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Summary:Detecting vehicles in video is a challenging problem owing to the motion of vehicles, the camera and the background and to variations of speed. This paper proposes a classifier based supervised method to detect moving vehicles from a moving camera. Dense scale invariant feature transform (dense SIFT) descriptors are used as features to describe the pattern of the object. And Extreme Learning Machine provides excellent generalization performance at fast speed. Our sample images taken by a camera in helicopter include 2000 images. Experiment results shown that this proposed method has not only good overall performance but also low computational cost.
DOI:10.1109/ROBIO.2015.7419002