SiamMan: Siamese Motion-aware Network for Visual Tracking

Abstract

In this paper, we present a novel siamese motion-aware network (SiamMan) for visual tracking, which consists of the siamese feature extraction subnetwork, followed by the classification, regression, and localization branches in parallel. The classification branch is used to distinguish the foreground from background, and the regression branch is adopt to regress the bounding box of target. To reduce the impact of manually designed anchor boxes to adapt to different target motion patterns, we design the localization branch, which aims to coarsely localize the target to help the regression branch to generate accurate results. Meanwhile, we introduce the global context module into the localization branch to capture long-range dependency for more robustness in large displacement of target. In addition, we design a multi-scale learnable attention module to guide these three branches to exploit discriminative features for better performance. The whole network is trained offline in an end-to-end fashion with large-scale image pairs using the standard SGD algorithm with back-propagation. Extensive experiments on five challenging benchmarks, i.e., VOT2016, VOT2018, OTB100, UAV123 and LTB35, demonstrate that SiamMan achieves leading accuracy with high efficiency. Code can be found at https://isrc. iscas.ac.cn/gitlab/research/siamman.

3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds

“…In [2], Bertinetto et al proposed SiamFC, a pioneering work that combines naive feature correlation with a fullyconvolutional Siamese network for object tracking. Subsequently, some improvements [85,68,75,82,76] are made to Siamese trackers, such as combining with a region proposal network [17,39,80,65] or an anchor-free FCOS detector [11], using a deeper architecture [38] or two-branch structure [23], exploiting attention [67,84] or self-attention [7], applying triplet loss [14]. However, these methods are specially designed for 2D object tracking, so they cannot be directly applied to 3D point clouds.…”