Object Tracking in Image Sequences using Point Features

This paper presents an object tracking technique based on the Bayesian Multiple Hypothesis Tracking (MHT) approach. Two algorithms, both based on the MHT technique are combined to generate an object tracker. The first MHT algorithm is employed for contour segmentation (based on an edge map). The second MHT algorithm is used in the temporal tracking of a selected object from the initial frame. An object is represented by key feature points that are extracted from it. The key points (mostly corner points) are detected using information obtained from the edge map. These key points are then tracked through the sequence. To confirm the correctness of the tracked key points, the location of the key points on the trajectory are verified against the segmented object identified in each frame. The results show that the tracker proposed can successfully track simple identifiable objects through an image sequence

Contour tracking with automatic motion model switching

Copyright © 2003 Pattern Recognition Society.In this paper we present an efficient contour-tracking algorithm which can track 2D silhouette of objects in extended image sequences. We demonstrate the ability of the tracker by tracking highly deformable contours (such as walking people) captured by a static camera. We represent contours (silhouette) of moving objects by using a cubic B-spline. The tracking algorithm is based on tracking a lower dimensional shape space (as opposed to tracking in spline space). Tracking the lower dimensional space has proved to be fast and efficient. The tracker is also coupled with an automatic motion-model switching algorithm, which makes the tracker robust and reliable when the object of interest is moving with multiple motion. The model-based tracking technique provided is capable of tracking rigid and non-rigid object contours with good tracking accuracy. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.P. Tissainayagam, D. Sute

Assessing the performance of corner detectors for point feature tracking applications

Copyright © 2004 Elsevier B.V. All rights reserved.In this paper we assess the performance of a variety of corner (point) detecting algorithms for feature tracking applications. We analyze four different types of corner extractors, which have been widely used for a variety of applications (they are described later in the paper). We use corner stability and corner localization properties as measures to evaluate the quality of the features extracted by the four detectors. For effective assessment of the corner detectors, first, we employed image sequences with no motion (simply static image sequences), so that the appearance and disappearance of corners in each frame is purely due to image plane noise and illumination conditions. The second stage included experiments on sequences with small motion. The experiments were devised to make the testing environment ideal to analyze the stability and localization properties of the corners extracted. The corners detected from the initial frame are then matched through the sequence using a corner matching strategy. We employed two different types of matchers, namely the GVM (Gradient Vector Matcher) and the Product Moment Coefficient Matcher (PMCM). Each of the corner detectors was tested with each of the matching algorithms to evaluate their performance in tracking (matching) the features. The experiments were carried out on a variety of image sequences with and without motion. © 2004 Elsevier B.V. All rights reserved.P. Tissainayagam and D. Sute

Assessing the performance of corner detectors for point feature tracking applications

In this paper we assess the performance of corner feature detecting algorithms for feature tracking applications. We analyze four different types of corner extractors, which have been widely used for a variety of applications. They are the Kitchen-Rosenfeld, the Harris, the Kanade-Lucas-Tomasi, and the Smith corner detectors. We use corner stability and corner localization properties as measures to evaluate the quality of the features extracted by the 4 detectors. For effective assessment of the corner detectors, we employed image sequences with no motion (simply static image sequences), so that the appearance and disappearance of corners in each frame is purely due to image plane noise and illumination conditions. Such a setup is ideal to analyze the stability and localization properties of the corners. The corners extracted from the initial frame are then matched through the sequence using a corner matching strategy. We employed 2 different types of matchers, namely the GVM (Gradient Vector Matcher) and the Product Moment Coefficient Matcher (PMCM). Each of the corner detectors was tested with each of the matching algorithms to evaluate their performance in tracking (matching) the features. The experiments were carried out on a variety of image sequences

Object tracking in image sequences using point features

Copyright © 2005 Pattern Recognition Society Published by Elsevier B.V.This paper presents an object tracking technique based on the Bayesian multiple hypothesis tracking (MHT) approach. Two algorithms, both based on the MHT technique are combined to generate an object tracker. The first MHT algorithm is employed for contour segmentation. The segmentation of contours is based on an edge map. The segmented contours are then merged to form recognisable objects. The second MHT algorithm is used in the temporal tracking of a selected object from the initial frame. An object is represented by key feature points that are extracted from it. The key points (mostly corner points) are detected using information obtained from the edge map. These key points are then tracked through the sequence. To confirm the correctness of the tracked key points, the location of the key points on the trajectory are verified against the segmented object identified in each frame. If an acceptable number of key-points lie on or near the contour of the object in a particular frame (n-th frame), we conclude that the selected object has been tracked (identified) successfully in frame n.P. Tissainayagam and D. Sute