Robust Correlation Tracking via Multi-channel Fused Features and Reliable Response Map

• URL: http://arxiv.org/abs/2011.12550v1
• Date: Wed, 25 Nov 2020 07:15:03 GMT
• Title: Robust Correlation Tracking via Multi-channel Fused Features and Reliable Response Map
• Authors: Xizhe Xue and Ying Li and Qiang Shen
• Abstract summary: This paper proposes a robust correlation tracking algorithm (RCT) based on two ideas. First, we propose a method to fuse features in order to more naturally describe the gradient and color information of the tracked object. Second, we present a novel strategy to significantly reduce noise in the response map and therefore ease the problem of model drift.
• Score: 10.079856376445598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Benefiting from its ability to efficiently learn how an object is changing, correlation filters have recently demonstrated excellent performance for rapidly tracking objects. Designing effective features and handling model drifts are two important aspects for online visual tracking. This paper tackles these challenges by proposing a robust correlation tracking algorithm (RCT) based on two ideas: First, we propose a method to fuse features in order to more naturally describe the gradient and color information of the tracked object, and introduce the fused features into a background aware correlation filter to obtain the response map. Second, we present a novel strategy to significantly reduce noise in the response map and therefore ease the problem of model drift. Systematic comparative evaluations performed over multiple tracking benchmarks demonstrate the efficacy of the proposed approach.

Related papers

• Leveraging the Power of Data Augmentation for Transformer-based Tracking [64.46371987827312]
  We propose two data augmentation methods customized for tracking. First, we optimize existing random cropping via a dynamic search radius mechanism and simulation for boundary samples. Second, we propose a token-level feature mixing augmentation strategy, which enables the model against challenges like background interference.
  arXiv  Detail & Related papers  (2023-09-15T09:18:54Z)
• TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection [21.04812985569116]
  We propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet. TFDet achieves state-of-the-art performance on two multispectral pedestrian benchmarks, KAIST and LLVIP.
  arXiv  Detail & Related papers  (2023-05-26T02:09:48Z)
• Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework [76.70603443624012]
  We propose a novel one-stream tracking (OSTrack) framework that unifies feature learning and relation modeling. In this way, discriminative target-oriented features can be dynamically extracted by mutual guidance. OSTrack achieves state-of-the-art performance on multiple benchmarks, in particular, it shows impressive results on the one-shot tracking benchmark GOT-10k.
  arXiv  Detail & Related papers  (2022-03-22T18:37:11Z)
• 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)
• Weakly Supervised Video Salient Object Detection [79.51227350937721]
  We present the first weakly supervised video salient object detection model based on relabeled "fixation guided scribble annotations" An "Appearance-motion fusion module" and bidirectional ConvLSTM based framework are proposed to achieve effective multi-modal learning and long-term temporal context modeling.
  arXiv  Detail & Related papers  (2021-04-06T09:48:38Z)
• DEFT: Detection Embeddings for Tracking [3.326320568999945]
  We propose an efficient joint detection and tracking model named DEFT. Our approach relies on an appearance-based object matching network jointly-learned with an underlying object detection network. DEFT has comparable accuracy and speed to the top methods on 2D online tracking leaderboards.
  arXiv  Detail & Related papers  (2021-02-03T20:00:44Z)
• Finding Action Tubes with a Sparse-to-Dense Framework [62.60742627484788]
  We propose a framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner. We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and UCFSports benchmark datasets.
  arXiv  Detail & Related papers  (2020-08-30T15:38:44Z)
• Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking [12.292672531693794]
  This work proposes a novel approach with dynamic consistency pursued correlation filters, i.e., the CPCF tracker. By minimizing the difference between the practical and the scheduled ideal consistency map, the consistency level is constrained to maintain temporal smoothness. The proposed tracker favorably surpasses the other 25 state-of-the-art trackers with real-time running speed ($sim$43FPS) on a single CPU.
  arXiv  Detail & Related papers  (2020-08-09T10:22:52Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)
• Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking [34.09763324745818]
  This paper aims to evaluate the performance of twelve state-of-the-art ResNet-based FENs in a DCF-based framework. It ranks their best feature maps and explores the generalized adoption of the best ResNet-based FEN into another DCF-based method. The proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation.
  arXiv  Detail & Related papers  (2020-04-03T05:33:59Z)

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.