Correlation filter-based tracking has exhibited impressive robustness and accuracy in recent years. Standard correlation filter-based trackers are restricted to translation estimation and equipped with fixed target response. These trackers produce an inferior performance when encountered with a significant scale variation or appearance change. We propose a log-polar mapping-based scale space tracker with an adaptive target response. This tracker transforms the scale variation of the target in the Cartesian space into a shift along the logarithmic axis in the log-polar space. A one-dimensional scale correlation filter is learned online to estimate the shift along the logarithmic axis. With the log-polar representation, scale estimation is achieved accurately without a multiresolution pyramid. To achieve an adaptive target response, a variance of the Gaussian function is computed from the response map and updated online with a learning rate parameter. Our log-polar mapping-based scale correlation filter and adaptive target response can be combined with any correlation filter-based trackers. In addition, the scale correlation filter can be extended to a two-dimensional correlation filter to achieve joint estimation of the scale variation and in-plane rotation. Experiments performed on an OTB50 benchmark demonstrate that our tracker achieves superior performance against state-of-the-art trackers.
Various approaches have been proposed for robust visual tracking, among which compressive tracking (CT) yields promising performance. In CT, Haar-like features are efficiently extracted with a very sparse measurement matrix and modeled as an online updated naïve Bayes classifier to account for target appearance change. The naïve Bayes classifier ignores overlap between Haar-like features and assumes that Haar-like features are independently distributed, which leads to drift in complex scenario. To address this problem, we present an extended CT algorithm, which assumes that all Haar-like features are correlated with each other and have multivariate Gaussian distribution. The mean vector and covariance matrix of multivariate normal distribution are incrementally updated with constant computational complexity to adapt to target appearance change. Each frame is associated with a temporal weight to expend less modeling power on old observation. Based on temporal weight, an update scheme with changing but convergent learning rate is derived with strict mathematic proof. Compared with CT, our extended algorithm achieves a richer representation of target appearance. The incremental multivariate Gaussian distribution is integrated into the particle filter framework to achieve better tracking performance. Extensive experiments on the CVPR2013 tracking benchmark demonstrate that our proposed tracker achieves superior performance both qualitatively and quantitatively over several state-of-the-art trackers.
A flexible new technique is proposed to calibrate the geometric model of line scan cameras. In this technique, the line scan camera is rigidly coupled to a calibrated frame camera to establish a pair of stereo cameras. The linear displacements and rotation angles between the two cameras are fixed but unknown. This technique only requires the pair of stereo cameras to observe a specially designed planar pattern shown at a few (at least two) different orientations. At each orientation, a stereo pair is obtained including a linear array image and a frame image. Radial distortion of the line scan camera is modeled. The calibration scheme includes two stages. First, point correspondences are established from the pattern geometry and the projective invariance of cross-ratio. Second, with a two-step calibration procedure, the intrinsic parameters of the line scan camera are recovered from several stereo pairs together with the rigid transform parameters between the pair of stereo cameras. Both computer simulation and real data experiments are conducted to test the precision and robustness of the calibration algorithm, and very good calibration results have been obtained. Compared with classical techniques which use three-dimensional calibration objects or controllable moving platforms, our technique is affordable and flexible in close-range photogrammetric applications.
Three-dimensional (3D) reconstruction is one of the most attractive research topics in photogrammetry and computer vision. Nowadays 3D reconstruction with simple and consumable equipment plays an important role. In this paper, a 3D reconstruction desktop system is built based on binocular stereo vision using a laser scanner. The hardware requirements are a simple commercial hand-held laser line projector and two common webcams for image acquisition. Generally, 3D reconstruction based on passive triangulation methods requires point correspondences among various viewpoints. The development of matching algorithms remains a challenging task in computer vision. In our proposal, with the help of a laser line projector, stereo correspondences are established robustly from epipolar geometry and the laser shadow on the scanned object. To establish correspondences more conveniently, epipolar rectification is employed using Bouguet’s method after stereo calibration with a printed chessboard. 3D coordinates of the observed points are worked out with rayray triangulation and reconstruction outliers are removed with the planarity constraint of the laser plane. Dense 3D point clouds are derived from multiple scans under different orientations. Each point cloud is derived by sweeping the laser plane across the object requiring 3D reconstruction. The Iterative Closest Point algorithm is employed to register the derived point clouds. Rigid body transformation between neighboring scans is obtained to get the complete 3D point cloud. Finally polygon meshes are reconstructed from the derived point cloud and color images are used in texture mapping to get a lifelike 3D model. Experiments show that our reconstruction method is simple and efficient.