Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking

Abstract

Applications for real-time visual tracking can be found in many areas, including visual odometry and augmented reality. Interest point detection and feature description form the basis of feature-based tracking, and a variety of algorithms for these tasks have been proposed. In this work, we present (1) a carefully designed dataset of video sequences of planar textures with ground truth, which includes various geometric changes, lighting conditions, and levels of motion blur, and which may serve as a testbed for a variety of tracking-related problems, and (2) a comprehensive quantitative evaluation of detector-descriptor-based visual camera tracking based on this testbed. We evaluate the impact of individual algorithm parameters, compare algorithms for both detection and description in isolation, as well as all detector-descriptor combinations as a tracking solution. In contrast to existing evaluations, which aim at different tasks such as object recognition and have limited validity for visual tracking, our evaluation is geared towards this application in all relevant factors (performance measures, testbed, candidate algorithms). To our knowledge, this is the first work that comprehensively compares these algorithms in this context, and in particular, on video streams.

Fast and scalable keypoint recognition and image retrieval using binary codes

“…The first is a set of video sequences with continuous motion used for evaluation of detectors and descriptors for visual tracking, from the University of California, Santa Barbara [7]. This dataset includes videos of six different planar tracking targets and several different motion patterns.…”

Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

“…Feature-based methods (i) detect key-points like corners and only extract feature descriptors from their vicinity; (ii) perform complex transformations into feature descriptors and match feature descriptors between images; and (iii) typically require high-resolution images [1][2][3][4][5]. A number of feature detectors and descriptors have been developed, examples being the wide-spread FAST detector [6] and the SIFT [7] and SURF detectors and descriptors [8], and the modern binary descriptors such as BRIEF [9,10], ORB [11], or FREAK [12].…”

“…However, we think that the insights on illumination tolerance for different distance measures gained in this paper are an important step to make holistic methods more competitive, at least for the min-warping method investigated here. Moreover, for feature-based methods, a number of studies have been dedicated to the comparative evaluation of different feature detectors and descriptors for different applications [1,2,5,[17][18][19] indicating that there may be no single method suitable for all applications. A systematic study exploring the application of feature-based methods for local visual homing with panoramic images is not yet available, and would also be beyond the scope of this paper.…”

“…In these methods, an image patch is first transformed into an invariant feature descriptor which is then matched by some simpler distance measure (reviews: [1][2][3][4][5][17][18][19]); here the burden of invariance (not only against illumination changes, but also against occlusions, spatial distortions, or noise) is imposed on the transformation. SIFT descriptors [7] comprise summed gradient magnitudes for different orientations in a histogram-like representation.…”

A Novel Method For Image Retrieval Based On Visually Significant Feature Point Maps

“…Recent approaches with regards to tracking could be found in [33], [34]. In [15], [16], interest point along with texture features have been used for image retrieval.…”