CoMaL Tracking: Tracking Points at the Object Boundaries

Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched. This is inefficient and may also lose many points that are not re-detected in the next frame. We propose a novel tracking algorithm to accurately and efficiently track CoMaL points. For this, the level line segment associated with the CoMaL points is matched to MSER segments in the next frame using shape-based matching and the matches are further filtered using texture-based matching. Experiments show improvements over a simple re-detect-and-match framework as well as KLT in terms of speed/accuracy on different real-world applications, especially at the object boundaries.Comment: 10 pages, 10 figures, to appear in 1st Joint BMTT-PETS Workshop on Tracking and Surveillance, CVPR 201

Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics

The purpose of this study is to provide a detailed performance comparison of feature detector/descriptor methods, particularly when their various combinations are used for image-matching. The localization experiments of a mobile robot in an indoor environment are presented as a case study. In these experiments, 3090 query images and 127 dataset images were used. This study includes five methods for feature detectors (features from accelerated segment test (FAST), oriented FAST and rotated binary robust independent elementary features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant feature transform (SIFT), and binary robust invariant scalable keypoints (BRISK)) and five other methods for feature descriptors (BRIEF, BRISK, SIFT, SURF, and ORB). These methods were used in 23 different combinations and it was possible to obtain meaningful and consistent comparison results using the performance criteria defined in this study. All of these methods were used independently and separately from each other as either feature detector or descriptor. The performance analysis shows the discriminative power of various combinations of detector and descriptor methods. The analysis is completed using five parameters: (i) accuracy, (ii) time, (iii) angle difference between keypoints, (iv) number of correct matches, and (v) distance between correctly matched keypoints. In a range of 60{\deg}, covering five rotational pose points for our system, the FAST-SURF combination had the lowest distance and angle difference values and the highest number of matched keypoints. SIFT-SURF was the most accurate combination with a 98.41% correct classification rate. The fastest algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to match 560 images captured during motion with 127 dataset images.Comment: 11 pages, 3 figures, 1 tabl

Comparison of affine-invariant local detectors and descriptors

International audienceIn this paper we summarize recent progress on local photometric invariants. The photometric invariants can be used to find correspondences in the presence of significant viewpoint changes. We evaluate the performance of region detectors and descriptors. We compare several methods for detecting affine regions [4, 9, 11, 18, 17]. We evaluate the repeatability of the detected regions, the accuracy of the detectors and the invariance to geometric as well as photometric image transformations. Furthermore, we compare several local descriptors [3, 5, 8, 14, 19]. The local descriptors are evaluated in terms of two properties: robustness and distinctiveness. The evaluation is carried out for different image transformations and scene types. We observe that the ranking of the detectors and descriptors remains the same regardless the scene type or image transformation

Comparison of affine-invariant local detectors and descriptors

International audienceIn this paper we summarize recent progress on local photometric invariants. The photometric invariants can be used to find correspondences in the presence of significant viewpoint changes. We evaluate the performance of region detectors and descriptors. We compare several methods for detecting affine regions [4, 9, 11, 18, 17]. We evaluate the repeatability of the detected regions, the accuracy of the detectors and the invariance to geometric as well as photometric image transformations. Furthermore, we compare several local descriptors [3, 5, 8, 14, 19]. The local descriptors are evaluated in terms of two properties: robustness and distinctiveness. The evaluation is carried out for different image transformations and scene types. We observe that the ranking of the detectors and descriptors remains the same regardless the scene type or image transformation