Object Preserving Siamese Network for Single Object Tracking on Point Clouds
Kaijie Zhao, Haitao Zhao, Zhongze Wang, Jingchao Peng, Zhengwei Hu

TL;DR
The paper introduces OPSNet, a novel 3D single object tracking method that preserves object points and features, significantly improving accuracy over previous Siamese-based trackers on KITTI and Waymo datasets.
Contribution
Proposes OPSNet, which enhances object feature preservation and sampling strategies to improve 3D object tracking accuracy.
Findings
Outperforms state-of-the-art on KITTI with 9.4% success gain.
Achieves 2.5% success improvement on Waymo dataset.
Effectively maintains object integrity during tracking.
Abstract
Obviously, the object is the key factor of the 3D single object tracking (SOT) task. However, previous Siamese-based trackers overlook the negative effects brought by randomly dropped object points during backbone sampling, which hinder trackers to predict accurate bounding boxes (BBoxes). Exploring an approach that seeks to maximize the preservation of object points and their object-aware features is of particular significance. Motivated by this, we propose an Object Preserving Siamese Network (OPSNet), which can significantly maintain object integrity and boost tracking performance. Firstly, the object highlighting module enhances the object-aware features and extracts discriminative features from template and search area. Then, the object-preserved sampling selects object candidates to obtain object-preserved search area seeds and drop the background points that contribute less to…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Face recognition and analysis · Virtual Reality Applications and Impacts
MethodsSiamese Network
