Vehicle Detection Based on Multi-feature Clues and Dempster-Shafer Fusion Theory

By Mahdi Rezaei, Mutsuhiro Terauchi. 2013

38 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Eduardo Almeida Soares
2. Plamen Parvanov Angelov
3. Bruno Costa
4. Marcos Castro

In Computer Vision and Pattern Recognition

1. Eduardo Almeida Soares
2. Plamen Parvanov Angelov
3. Bruno Costa
4. Marcos Castro

Citations in the corpus

• Towards Explainable Deep Neural Networks (xDNN) (2019, methodology): “We tested it on some well-known benchmark data sets such as iRoads [15] and Caltech-256 [16] and xDNN outperforms the other methods including deep learning in terms of accuracy, time to train, moreover, offers a clearly explainable classifier.”
• Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios (2019, methodology): “The iROADS dataset [31] was considered in the analysis.”
• Novelty Detection and Learning from Extremely Weak Supervision (2019, methodology): “In the first experiment the iRoads dataset [21, 20] was considered.”