Transforming Model Prediction for Tracking

• URL: http://arxiv.org/abs/2203.11192v1
• Date: Mon, 21 Mar 2022 17:59:40 GMT
• Title: Transforming Model Prediction for Tracking
• Authors: Christoph Mayer, Martin Danelljan, Goutam Bhat, Matthieu Paul, Danda Pani Paudel, Fisher Yu, Luc Van Gool
• Abstract summary: Transformers capture global relations with little inductive bias, allowing it to learn the prediction of more powerful target models. We train the proposed tracker end-to-end and validate its performance by conducting comprehensive experiments on multiple tracking datasets. Our tracker sets a new state of the art on three benchmarks, achieving an AUC of 68.5% on the challenging LaSOT dataset.
• Score: 109.08417327309937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function. While this inductive bias integrates valuable domain knowledge, it limits the expressivity of the tracking network. In this work, we therefore propose a tracker architecture employing a Transformer-based model prediction module. Transformers capture global relations with little inductive bias, allowing it to learn the prediction of more powerful target models. We further extend the model predictor to estimate a second set of weights that are applied for accurate bounding box regression. The resulting tracker relies on training and on test frame information in order to predict all weights transductively. We train the proposed tracker end-to-end and validate its performance by conducting comprehensive experiments on multiple tracking datasets. Our tracker sets a new state of the art on three benchmarks, achieving an AUC of 68.5% on the challenging LaSOT dataset.

Related papers

• RealTraj: Towards Real-World Pedestrian Trajectory Forecasting [10.332817296500533]
  We propose a novel framework, RealTraj, that enhances the real-world applicability of trajectory forecasting. We present Det2TrajFormer, a trajectory forecasting model that remains invariant in tracking noise by using past detections as inputs. Unlike previous trajectory forecasting methods, our approach fine-tunes the model using only ground-truth detections, significantly reducing the need for costly person ID annotations.
  arXiv  Detail & Related papers  (2024-11-26T12:35:26Z)
• Robust Visual Tracking via Iterative Gradient Descent and Threshold Selection [4.978166837959101]
  We introduce a novel robust linear regression estimator, which achieves favorable performance when the error vector follows i.i.d Gaussian-Laplacian distribution. In addition, we expend IGDTS to a generative tracker, and apply IGDTS-distance to measure the deviation between the sample and the model. Experimental results on several challenging image sequences show that the proposed tracker outperformance existing trackers.
  arXiv  Detail & Related papers  (2024-06-02T01:51:09Z)
• Certified Human Trajectory Prediction [66.1736456453465]
  Tray prediction plays an essential role in autonomous vehicles. We propose a certification approach tailored for the task of trajectory prediction. We address the inherent challenges associated with trajectory prediction, including unbounded outputs, and mutli-modality.
  arXiv  Detail & Related papers  (2024-03-20T17:41:35Z)
• Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction [15.454206825258169]
  Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians. This paper proposes a graph transformer structure to improve prediction performance.
  arXiv  Detail & Related papers  (2024-01-10T01:50:29Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Towards A Foundation Model For Trajectory Intelligence [0.0]
  We present the results of training a large trajectory model using real-world user check-in data. Our approach follows a pre-train and fine-tune paradigm, where a base model is pre-trained via masked trajectory modeling. Our empirical analysis utilizes a comprehensive dataset of over 2 billion check-ins generated by more than 6 million users.
  arXiv  Detail & Related papers  (2023-11-30T00:34:09Z)
• 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)
• An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images [11.733622044569486]
  We build an end-to-end framework for detection, tracking, and trajectory prediction called ODTP. It adopts the state-of-the-art online multi-object tracking model, QD-3DT, for perception and trains the trajectory predictor, DCENet++, directly based on the detection results. We evaluate the performance of ODTP on the widely used nuScenes dataset for autonomous driving.
  arXiv  Detail & Related papers  (2023-08-09T15:46:25Z)
• PnPNet: End-to-End Perception and Prediction with Tracking in the Loop [82.97006521937101]
  We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. We propose Net, an end-to-end model that takes as input sensor data, and outputs at each time step object tracks and their future level.
  arXiv  Detail & Related papers  (2020-05-29T17:57:25Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.