Evaluation of Object Detection Proposals Under Condition Variations

TL;DR

This paper evaluates object detection proposal methods under environmental variations, introducing a new combined approach that outperforms existing methods in challenging conditions like illumination and viewpoint changes.

Contribution

A novel object detection proposal method combining Selective Search and EdgeBoxes, tested under environmental variations, showing improved robustness.

Findings

Combination method outperforms individual methods under illumination changes.

Combination method outperforms individual methods under viewpoint variations.

The study provides insights into method robustness in environmental changes.

Abstract

Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain. This process gives insight into the capabilities of methods in handling environmental changes. In this paper, a new method for object detection is introduced that combines the Selective Search and EdgeBoxes. We tested these three methods under environmental variations. Our experiments demonstrate the outperformance of the combination method under illumination and view point variations.