Bilateral Symmetry Detection for Real-time Robotics Applications

Abstract

Bilateral symmetry is a salient visual feature of many man-made objects. In this paper, we present research that uses bilateral symmetry to identify, segment and track objects in real time using vision. Apart from the assumption of symmetry, the algorithms presented do not require any object models, such as color, shape or three-dimensional primitives. In order to counter the high computational cost of traditional symmetry detection methods, a novel computationally efficient algorithm is proposed. To investigate symmetry as an object feature, our fast detection scheme is applied to the tasks of object detection, segmentation and tracking. We find that objects with a line of symmetry can be segmented without relying on color or shape models by using a dynamic programming approach. Object tracking is achieved by estimating symmetry line parameters using a Kalman filter. The tracker operates at 40 frames per second on 640 x 480 video while running on a standard laptop PC. We use 10 difficult real-world tracking sequences to test our approach. We also quantitatively analyze symmetry as a tracking feature by comparing detected symmetry lines against ground truth. Color tracking is also performed to provide a qualitative comparison.