Multi-Template Temporal Siamese Network for Long-Term Object Tracking

• URL: http://arxiv.org/abs/2211.13812v1
• Date: Thu, 24 Nov 2022 22:07:33 GMT
• Title: Multi-Template Temporal Siamese Network for Long-Term Object Tracking
• Authors: Ali Sekhavati and Won-Sook Lee
• Abstract summary: Siamese Network based trackers use the first frame as the ground truth of an object and fail when target appearance changes significantly in next frames. We propose two ideas to solve both problems. This tracker achieves state-of-the-art performance on the long-term tracking dataset UAV20L by improving the success rate by a large margin of 15%.
• Score: 0.6853165736531939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Siamese Networks are one of most popular visual object tracking methods for their high speed and high accuracy tracking ability as long as the target is well identified. However, most Siamese Network based trackers use the first frame as the ground truth of an object and fail when target appearance changes significantly in next frames. They also have dif iculty distinguishing the target from similar other objects in the frame. We propose two ideas to solve both problems. The first idea is using a bag of dynamic templates, containing diverse, similar, and recent target features and continuously updating it with diverse target appearances. The other idea is to let a network learn the path history and project a potential future target location in a next frame. This tracker achieves state-of-the-art performance on the long-term tracking dataset UAV20L by improving the success rate by a large margin of 15% (65.4 vs 56.6) compared to the state-of-the-art method, HiFT. The of icial python code of this paper is publicly available.

Related papers

• Two-stream Beats One-stream: Asymmetric Siamese Network for Efficient Visual Tracking [54.124445709376154]
  We propose a novel asymmetric Siamese tracker named textbfAsymTrack for efficient tracking. Building on this architecture, we devise an efficient template modulation mechanism to inject crucial cues into the search features. Experiments demonstrate that AsymTrack offers superior speed-precision trade-offs across different platforms.
  arXiv  Detail & Related papers  (2025-03-01T14:44:54Z)
• Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance [87.19164603145056]
  We propose LoRAT, a method that unveils the power of large ViT model for tracking within laboratory-level resources. The essence of our work lies in adapting LoRA, a technique that fine-tunes a small subset of model parameters without adding inference latency. We design an anchor-free head solely based on to adapt PETR, enabling better performance with less computational overhead.
  arXiv  Detail & Related papers  (2024-03-08T11:41:48Z)
• A new hope for network model generalization [66.5377859849467]
  Generalizing machine learning models for network traffic dynamics tends to be considered a lost cause. An ML architecture called_Transformer_ has enabled previously unimaginable generalization in other domains. We propose a Network Traffic Transformer (NTT) to learn network dynamics from packet traces.
  arXiv  Detail & Related papers  (2022-07-12T21:16:38Z)
• Context-aware Visual Tracking with Joint Meta-updating [11.226947525556813]
  We propose a context-aware tracking model to optimize the tracker over the representation space, which jointly meta-update both branches by exploiting information along the whole sequence. The proposed tracking method achieves an EAO score of 0.514 on VOT2018 with the speed of 40FPS, demonstrating its capability of improving the accuracy and robustness of the underlying tracker with little speed drop.
  arXiv  Detail & Related papers  (2022-04-04T14:16:00Z)
• Unified Transformer Tracker for Object Tracking [58.65901124158068]
  We present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both Single Object Tracking (SOT) and Multiple Object Tracking (MOT)
  arXiv  Detail & Related papers  (2022-03-29T01:38:49Z)
• Updatable Siamese Tracker with Two-stage One-shot Learning [10.13621503834501]
  offline Siamese networks have achieved very promising tracking performance, especially in accuracy and efficiency. Traditional updaters are difficult to process the irregular variations and sampling noises of objects, so it is quite risky to adopt them to update Siamese networks. In this paper, we first present a two-stage one-shot learner, which can predict the local parameters of primary classifier with object samples from diverse stages. Then, an updatable Siamese network is proposed based on the learner (SiamTOL), which is able to complement online update by itself.
  arXiv  Detail & Related papers  (2021-04-30T15:18:41Z)
• STMTrack: Template-free Visual Tracking with Space-time Memory Networks [42.06375415765325]
  Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance. We propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target. Specifically, a novel memory mechanism is introduced, which stores the historical information of the target to guide the tracker to focus on the most informative regions in the current frame.
  arXiv  Detail & Related papers  (2021-04-01T08:10:56Z)
• Multiple Convolutional Features in Siamese Networks for Object Tracking [13.850110645060116]
  Multiple Features-Siamese Tracker (MFST) is a novel tracking algorithm exploiting several hierarchical feature maps for robust tracking. MFST achieves high tracking accuracy, while outperforming the standard siamese tracker on object tracking benchmarks.
  arXiv  Detail & Related papers  (2021-03-01T08:02:27Z)
• MFST: Multi-Features Siamese Tracker [13.850110645060116]
  Multi-Features Siamese Tracker (MFST) is a novel tracking algorithm exploiting several hierarchical feature maps for robust deep similarity tracking. MFST achieves high tracking accuracy, while outperforming several state-of-the-art trackers, including standard Siamese trackers.
  arXiv  Detail & Related papers  (2021-03-01T07:18:32Z)
• Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking [79.80401607146987]
  Existing object tracking usually learns a bounding-box based template to match visual targets across frames, which cannot accurately learn a pixel-wise representation. This paper presents a novel segmentation-based tracking architecture, which is equipped with a local-temporal memory network to learn accurate-temporal correspondence.
  arXiv  Detail & Related papers  (2020-09-21T08:12:02Z)
• Tracking by Instance Detection: A Meta-Learning Approach [99.66119903655711]
  We propose a principled three-step approach to build a high-performance tracker. We build two trackers, named Retina-MAML and FCOS-MAML, based on two modern detectors RetinaNet and FCOS. Both trackers run in real-time at 40 FPS.
  arXiv  Detail & Related papers  (2020-04-02T05:55:06Z)
• High-Performance Long-Term Tracking with Meta-Updater [75.80564183653274]
  Long-term visual tracking has drawn increasing attention because it is much closer to practical applications than short-term tracking. Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, thus, they cannot benefit from great progress of short-term trackers with online update. We propose a novel offline-trained Meta-Updater to address an important but unsolved problem: Is the tracker ready for updating in the current frame?
  arXiv  Detail & Related papers  (2020-04-01T09:29:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.