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SeqTrack: Sequence to Sequence Learning for Visual Object Tracking
Computer Vision and Pattern Recognition, 2023In this paper, we present a new sequence-to-sequence learning framework for visual tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem, which predicts object bounding boxes in an autoregressive fashion.
Xin Chen +4 more
semanticscholar +1 more source
Improving Multiple Object Tracking with Single Object Tracking
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance. The major reason for this situation is that it is inappropriate and inefficient to apply multiple SOT models directly to the MOT task,
Linyu Zheng +5 more
openaire +1 more source
FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
International Journal of Computer Vision, 2020Multi-object tracking (MOT) is an important problem in computer vision which has a wide range of applications. Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the
Yifu Zhang +4 more
semanticscholar +1 more source
MOTR: End-to-End Multiple-Object Tracking with TRansformer
European Conference on Computer Vision, 2021Temporal modeling of objects is a key challenge in multiple object tracking (MOT). Existing methods track by associating detections through motion-based and appearance-based similarity heuristics. The post-processing nature of association prevents end-to-
Fangao Zeng +5 more
semanticscholar +1 more source
Fully-Convolutional Siamese Networks for Object Tracking
ECCV Workshops, 2016The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object’s appearance exclusively online, using as sole training data the video itself.
Luca Bertinetto +4 more
semanticscholar +1 more source
SmallTrack: Wavelet Pooling and Graph Enhanced Classification for UAV Small Object Tracking
IEEE Transactions on Geoscience and Remote Sensing, 2023Aerial object tracking has recently shown great potential in the field of remote sensing. However, small objects with limited feature information pose a huge challenge to aerial trackers.
Yuanliang Xue +6 more
semanticscholar +1 more source
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure [1] and it populates the majority of over
Lianghua Huang, Xin Zhao, Kaiqi Huang
semanticscholar +1 more source
3D Multi-Object Tracking With Adaptive Cubature Kalman Filter for Autonomous Driving
IEEE Transactions on Intelligent Vehicles, 2023A crucial and challenging issue in autonomous driving is dynamic road environment detection and 3D multi object tracking. In this article, we propose a novel framework for online 3D multi-object tracking to eliminate the influence of inherent uncertainty
Ge Guo, Shijie Zhao
semanticscholar +1 more source
SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking
ECCV Workshops, 20213D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the"tracking-by-detection"paradigm.
Ziqi Pang, Zhichao Li, Naiyan Wang
semanticscholar +1 more source
Spiking Transformers for Event-based Single Object Tracking
Computer Vision and Pattern Recognition, 2022Event-based cameras bring a unique capability to tracking, being able to function in challenging real-world conditions as a direct result of their high temporal resolution and high dynamic range.
Jiqing Zhang +6 more
semanticscholar +1 more source