Algorithmic optimisation of histogram intersection kernel support vector machine‐based pedestrian detection using low complexity features
Histogram intersection kernel support vector machine (SVM) is accepted as a better discriminator than its linear counterpart when used for pedestrian detection in images and video frames. Its computational complexity has, however, limited its use in practical real‐time detectors. To circumvent this...
Main Author: | Muhammad Bilal |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2017-08-01
|
Series: | IET Computer Vision |
Subjects: | |
Online Access: | https://doi.org/10.1049/iet-cvi.2016.0403 |
Similar Items
-
Partially occluded pedestrian classification using histogram of oriented gradients and local weighted linear kernel support vector machine
by: Saleh Aly
Published: (2014-12-01) -
Modified Multi-Kernel Support Vector Machine for Mask Detection
by: Muhammad Athoillah, et al.
Published: (2022-06-01) -
Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics
by: Rini Nuraini, et al.
Published: (2023-02-01) -
Using Support Vector Machines and Bayesian Filtering for Classifying Agent Intentions at Road Intersections
by: Aoude, Georges S., et al.
Published: (2009) -
Group cost-sensitive BoostLR with vector form decorrelated filters for pedestrian detection
by: Zhou, Chengju, et al.
Published: (2021)