Can Deep Learning be Applied to Model-Based Multi-Object Tracking?

• URL: http://arxiv.org/abs/2202.07909v1
• Date: Wed, 16 Feb 2022 07:43:08 GMT
• Title: Can Deep Learning be Applied to Model-Based Multi-Object Tracking?
• Authors: Juliano Pinto, Georg Hess, William Ljungbergh, Yuxuan Xia, Henk Wymeersch, Lennart Svensson
• Abstract summary: Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements. Deep learning (DL) has been increasingly used in MOT for improving tracking performance. In this paper, we propose a Transformer-based DL tracker and evaluate its performance in the model-based setting.
• Score: 25.464269324261636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense systems, and others. In recent years, deep learning (DL) has been increasingly used in MOT for improving tracking performance, but mostly in settings where the measurements are high-dimensional and there are no available models of the measurement likelihood and the object dynamics. The model-based setting instead has not attracted as much attention, and it is still unclear if DL methods can outperform traditional model-based Bayesian methods, which are the state of the art (SOTA) in this context. In this paper, we propose a Transformer-based DL tracker and evaluate its performance in the model-based setting, comparing it to SOTA model-based Bayesian methods in a variety of different tasks. Our results show that the proposed DL method can match the performance of the model-based methods in simple tasks, while outperforming them when the task gets more complicated, either due to an increase in the data association complexity, or to stronger nonlinearities of the models of the environment.

Related papers

• 3D Multi-Object Tracking with Semi-Supervised GRU-Kalman Filter [6.13623925528906]
  3D Multi-Object Tracking (MOT) is essential for intelligent systems like autonomous driving and robotic sensing. We propose a GRU-based MOT method, which introduces a learnable Kalman filter into the motion module. This approach is able to learn object motion characteristics through data-driven learning, thereby avoiding the need for manual model design and model error.
  arXiv  Detail & Related papers  (2024-11-13T08:34:07Z)
• Data-Driven Approaches for Modelling Target Behaviour [1.5495593104596401]
  The performance of tracking algorithms depends on the chosen model assumptions regarding the target dynamics. This paper provides a comparative study between three different methods that use machine learning to describe the underlying object motion.
  arXiv  Detail & Related papers  (2024-10-14T14:18:27Z)
• Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing [20.99082981430798]
  Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and time-varying number of objects over a certain time window. Deep learning based algorithms are a possible venue for tackling this issue but have not been applied extensively in settings where accurate multi-object models are available. We propose a novel DL architecture specifically tailored for this setting that decouples the data association task from the smoothing task.
  arXiv  Detail & Related papers  (2023-12-22T20:24:39Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Towards Efficient Task-Driven Model Reprogramming with Foundation Models [52.411508216448716]
  Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only support a small model due to the limited computational resources or efficiency considerations. This brings a critical challenge for the real-world application of foundation models: one has to transfer the knowledge of a foundation model to the downstream task.
  arXiv  Detail & Related papers  (2023-04-05T07:28:33Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Which priors matter? Benchmarking models for learning latent dynamics [70.88999063639146]
  Several methods have proposed to integrate priors from classical mechanics into machine learning models. We take a sober look at the current capabilities of these models. We find that the use of continuous and time-reversible dynamics benefits models of all classes.
  arXiv  Detail & Related papers  (2021-11-09T23:48:21Z)
• Multitarget Tracking with Transformers [21.81266872964314]
  Multitarget Tracking (MTT) is a problem of tracking the states of an unknown number of objects using noisy measurements. In this paper, we propose a high-performing deep-learning method for MTT based on the Transformer architecture.
  arXiv  Detail & Related papers  (2021-04-01T19:14:55Z)
• Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting [0.8399688944263843]
  It is known that if models selected for data modelling are distinct (linear/non-linear, static/dynamic) and independent (minimally correlated) models, the accuracy of the predictions is improved. Various approaches suggested in the literature to weigh the ensemble models use a static set of weights. To address this issue, a Reinforcement Learning (RL) approach to dynamically assign and update weights of each of the models at different time instants.
  arXiv  Detail & Related papers  (2020-08-20T10:40:42Z)
• SoDA: Multi-Object Tracking with Soft Data Association [75.39833486073597]
  Multi-object tracking (MOT) is a prerequisite for a safe deployment of self-driving cars. We propose a novel approach to MOT that uses attention to compute track embeddings that encode dependencies between observed objects.
  arXiv  Detail & Related papers  (2020-08-18T03:40:25Z)

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.