Visual object categorization with new keypoint-based adaBoost features

TL;DR

This paper introduces a novel keypoint-based feature set for visual object categorization using adaBoost, achieving high accuracy and demonstrating the features' semantic relevance across different object categories.

Contribution

The paper presents a new family of keypoint-based features for adaBoost that are effective for object categorization and show semantic significance in feature selection.

Findings

95% recall and precision on car dataset

97% recall and 92% precision on pedestrian subset

Real-time vehicle detection using keypoints

Abstract

We present promising results for visual object categorization, obtained with adaBoost using new original ?keypoints-based features?. These weak-classifiers produce a boolean response based on presence or absence in the tested image of a ?keypoint? (a kind of SURF interest point) with a descriptor sufficiently similar (i.e. within a given distance) to a reference descriptor characterizing the feature. A first experiment was conducted on a public image dataset containing lateral-viewed cars, yielding 95% recall with 95% precision on test set. Preliminary tests on a small subset of a pedestrians database also gives promising 97% recall with 92 % precision, which shows the generality of our new family of features. Moreover, analysis of the positions of adaBoost-selected keypoints show that they correspond to a specific part of the object category (such as ?wheel? or ?side skirt? in the case of lateral-cars) and thus have a ?semantic? meaning. We also made a first test on video for detecting vehicles from adaBoostselected keypoints filtered in real-time from all detected keypoints.