Robust Object Detection Research Articles

Cue integration for visual servoing

The robustness and reliability of vision algorithms is, nowadays, the key issue in robotic research and industrial applications. To control a robot in a closed-loop fashion, different tracking systems have been reported in the literature. A common approach to increased robustness of a tracking system is the use of different models (CAD model of the object, motion model) known a priori. Our hypothesis is that fusion of multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. A particular application is the estimation of a robot's end-effector position in a sequence of images. The research investigates the following two different approaches to cue integration: 1) voting and 2) fuzzy logic-based fusion. The two approaches have been tested in association with scenes of varying complexity. Experimental results clearly demonstrate that fusion of cues results in a tracking system with a robust performance. The robustness is in particular evident for scenes with multiple moving objects and partial occlusion of the tracked object.

Object oriented face detection usingcolour transformation and range segmentation

A range segmentation technique using a matching pixel count (MPC) disparity histogram is combined with the generalised facial colour distribution (GFCD) skin-colour transform technique to provide robust multiple facial object detection with depth-range selectivity in a complex background environment.

Robust object detection under various environmental conditions

AbstractAn algorithm for object detection is presented. This method is robust against brightness variation and does not depend on the position, size, background pattern, shape, or color of target objects. The method consists of three processes: (1) normalize the brightness of the target and reference images; (2) calculate features, called the normalized principal component features; and (3) construct a classifier by statistical learning. The method is applied to car detection in a parking lot. Under various conditions, at least 98 percent of the cars were detected correctly. Application to moving‐car detection and person detection also is presented.

Developing texture-based image clutter measures for object detection

Automatic object detection is one of the basic tasks performed by an image understanding system. Object detection approaches need to perform accurately and robustly over a wide range of scenes. Although a number of detection approaches have been developed and reported, a need remains for standards by which to judge the relative merits of such approaches. Image characteristics, object characteristics, and detection methodology are recognized as the main variables affecting object detection. A basis for their quantitative analysis and evaluation is developed. This research keeps object detection methodology constant while varying image and object characteristics to develop a set of quantiative standards. This requires an ability to derive a quantitative measure for the observed in an image. A performance index for object detection approaches, as a function of scene nature, is valuable. Current approaches to image clutter or quality characterization are studied and a new measure based on image texture content and object characteristics is proposed. An extensive set of experimental studies is utilized to evaluate this texture-based image clutter (TIC) measure. TIC is shown to be better suited than other reported clutter measures because of its ability to accurately quantify perceptual effects and to serve as a robust indicator ofthe object detection and false alarm rates as a function of image clutter.