Joint detection and embedding (JDE) methods usually fuse the target motion information and appearance information as the data association matrix, which could fail when the target is briefly lost or blocked in multi-object tracking (MOT). In this paper, we aim to solve this problem by proposing a novel association matrix, the Embedding and GioU (EG) matrix, which combines the embedding cosine distance and GioU distance of objects. To improve the performance of data association, we develop a simple, effective, bottom-up fusion tracker for re-identity features, named SimpleTrack, and propose a new tracking strategy which can mitigate the loss of detection targets. To show the effectiveness of the proposed method, experiments are carried out using five different state-of-the-art JDE-based methods. The results show that by simply replacing the original association matrix with our EG matrix, we can achieve significant improvements in IDF1, HOTA and IDsw metrics, and increase the tracking speed of these methods by around 20%. In addition, our SimpleTrack has the best data association capability among the JDE-based methods, e.g., 61.6 HOTA and 76.3 IDF1, on the test set of MOT17 with 23 FPS running speed on a single GTX2080Ti GPU.
In most human made scenes, such as high-rise urban city or indoor environment, the surface normal vectors or direction vectors are concentrated in three orthogonal principal directions. The scene of such a pattern is called Manhattan World (MW), and the coordinate frame formed by the three principal directions is called Manhattan Frame (MF). MF estimation methods have been applied to many different fields, such as scene reconstruction, Visual based Simultaneous Localization And Mapping (V-SLAM) and camera calibration. In this paper, we propose a novel MF estimation method based on a set of normal vectors. A cost function of normal vectors and MF axes is introduced based on the trigonometric function. For computational purpose, the cost function is significantly simplified by making use of vector dot and cross products, and the reduced cost function only involves 14 scalar parameters that need to be computed with O(n) complexity. The experimental results show that the proposed MF estimation method has excellent real-time performance and gives high accuracy on both the virtual and real-world benchmark datasets of different sizes.
Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.
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