Multiple Convolutional Features in Siamese Networks for Object Tracking

• URL: http://arxiv.org/abs/2103.01222v1
• Date: Mon, 1 Mar 2021 08:02:27 GMT
• Title: Multiple Convolutional Features in Siamese Networks for Object Tracking
• Authors: Zhenxi Li, Guillaume-Alexandre Bilodeau, Wassim Bouachir
• Abstract summary: 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.
• Score: 13.850110645060116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are specifically designed to address the image similarity problem, and thus are inherently more appropriate for the tracking task. However, Siamese trackers mainly use the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not an optimal choice in a deep similarity framework. We present a Multiple Features-Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust tracking. Since convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to obtain a richer and more efficient representation of the target. Moreover, we handle the target appearance variations by calibrating the deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming the standard siamese tracker on object tracking benchmarks. The source code and trained models are available at https://github.com/zhenxili96/MFST.

Related papers

• Multi-attention Associate Prediction Network for Visual Tracking [3.9628431811908533]
  classification-regression prediction networks have realized impressive success in several modern deep trackers. There is an inherent difference between classification and regression tasks, so they have diverse even opposite demands for feature matching. We propose a multi-attention associate prediction network (MAPNet) to tackle the above problems.
  arXiv  Detail & Related papers  (2024-03-25T03:18:58Z)
• Correlation-Embedded Transformer Tracking: A Single-Branch Framework [69.0798277313574]
  We propose a novel single-branch tracking framework inspired by the transformer. Unlike the Siamese-like feature extraction, our tracker deeply embeds cross-image feature correlation in multiple layers of the feature network. The output features can be directly used for predicting target locations without additional correlation steps.
  arXiv  Detail & Related papers  (2024-01-23T13:20:57Z)
• Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking [59.79252390626194]
  We propose a novel solution named TransSTAM, which leverages Transformer to model both the appearance features of each object and the spatial-temporal relationships among objects. The proposed method is evaluated on multiple public benchmarks including MOT16, MOT17, and MOT20, and it achieves a clear performance improvement in both IDF1 and HOTA.
  arXiv  Detail & Related papers  (2022-05-31T01:19:18Z)
• Correlation-Aware Deep Tracking [83.51092789908677]
  We propose a novel target-dependent feature network inspired by the self-/cross-attention scheme. Our network deeply embeds cross-image feature correlation in multiple layers of the feature network. Our model can be flexibly pre-trained on abundant unpaired images, leading to notably faster convergence than the existing methods.
  arXiv  Detail & Related papers  (2022-03-03T11:53:54Z)
• Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking [82.34356879078955]
  We propose a compact memory embedding to enhance the discrimination of the segmentation-based deformable visual tracking method. Our method outperforms the excellent segmentation-based trackers, i.e., D3S and SiamMask on DAVIS 2017 benchmark.
  arXiv  Detail & Related papers  (2021-11-23T03:07:12Z)
• 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)
• Coarse-to-Fine Object Tracking Using Deep Features and Correlation Filters [2.3526458707956643]
  This paper presents a novel deep learning tracking algorithm. We exploit the generalization ability of deep features to coarsely estimate target translation. Then, we capitalize on the discriminative power of correlation filters to precisely localize the tracked object.
  arXiv  Detail & Related papers  (2020-12-23T16:43:21Z)
• 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)
• Segment as Points for Efficient Online Multi-Object Tracking and Segmentation [66.03023110058464]
  We propose a highly effective method for learning instance embeddings based on segments by converting the compact image representation to un-ordered 2D point cloud representation. Our method generates a new tracking-by-points paradigm where discriminative instance embeddings are learned from randomly selected points rather than images. The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods by large margins.
  arXiv  Detail & Related papers  (2020-07-03T08:29:35Z)

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.