ETTrack: Enhanced Temporal Motion Predictor for Multi-Object Tracking

• URL: http://arxiv.org/abs/2405.15755v1
• Date: Fri, 24 May 2024 17:51:33 GMT
• Title: ETTrack: Enhanced Temporal Motion Predictor for Multi-Object Tracking
• Authors: Xudong Han, Nobuyuki Oishi, Yueying Tian, Elif Ucurum, Rupert Young, Chris Chatwin, Philip Birch,
• Abstract summary: We propose a motion-based MOT approach with an enhanced temporal motion predictor, ETTrack. Specifically, the motion predictor integrates a transformer model and a Temporal Convolutional Network (TCN) to capture short-term and long-term motion patterns. We show ETTrack achieves a competitive performance compared with state-of-the-art trackers on DanceTrack and SportsMOT.
• Score: 4.250337979548885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many Multi-Object Tracking (MOT) approaches exploit motion information to associate all the detected objects across frames. However, many methods that rely on filtering-based algorithms, such as the Kalman Filter, often work well in linear motion scenarios but struggle to accurately predict the locations of objects undergoing complex and non-linear movements. To tackle these scenarios, we propose a motion-based MOT approach with an enhanced temporal motion predictor, ETTrack. Specifically, the motion predictor integrates a transformer model and a Temporal Convolutional Network (TCN) to capture short-term and long-term motion patterns, and it predicts the future motion of individual objects based on the historical motion information. Additionally, we propose a novel Momentum Correction Loss function that provides additional information regarding the motion direction of objects during training. This allows the motion predictor rapidly adapt to motion variations and more accurately predict future motion. Our experimental results demonstrate that ETTrack achieves a competitive performance compared with state-of-the-art trackers on DanceTrack and SportsMOT, scoring 56.4% and 74.4% in HOTA metrics, respectively.

Related papers

• Event-Based Tracking Any Point with Motion-Augmented Temporal Consistency [58.719310295870024]
  This paper presents an event-based framework for tracking any point. It tackles the challenges posed by spatial sparsity and motion sensitivity in events. It achieves 150% faster processing with competitive model parameters.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model [18.607106274732885]
  We introduce a Mamba-based motion model named Mamba moTion Predictor (MTP) MTP takes the spatial-temporal location dynamics of objects as input, captures the motion pattern using a bi-Mamba encoding layer, and predicts the next motion. Our proposed tracker, MambaTrack, demonstrates advanced performance on benchmarks such as Dancetrack and SportsMOT.
  arXiv  Detail & Related papers  (2024-08-17T11:58:47Z)
• DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction [15.542306419065945]
  We propose a real-time diffusion-based MOT approach named DiffMOT to tackle the complex non-linear motion. As a MOT tracker, the DiffMOT is real-time at 22.7FPS, and also outperforms the state-of-the-art on DanceTrack and SportsMOT datasets.
  arXiv  Detail & Related papers  (2024-03-04T14:21:51Z)
• SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features [52.213656737672935]
  SpikeMOT is an event-based multi-object tracker. SpikeMOT uses spiking neural networks to extract sparsetemporal features from event streams associated with objects.
  arXiv  Detail & Related papers  (2023-09-29T05:13:43Z)
• MotionTrack: Learning Motion Predictor for Multiple Object Tracking [68.68339102749358]
  We introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT.
  arXiv  Detail & Related papers  (2023-06-05T04:24:11Z)
• MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking [56.92165669843006]
  We propose MotionTrack, which learns robust short-term and long-term motions in a unified framework to associate trajectories from a short to long range. For dense crowds, we design a novel Interaction Module to learn interaction-aware motions from short-term trajectories, which can estimate the complex movement of each target. For extreme occlusions, we build a novel Refind Module to learn reliable long-term motions from the target's history trajectory, which can link the interrupted trajectory with its corresponding detection.
  arXiv  Detail & Related papers  (2023-03-18T12:38:33Z)
• Motion Transformer with Global Intention Localization and Local Movement Refinement [103.75625476231401]
  Motion TRansformer (MTR) models motion prediction as the joint optimization of global intention localization and local movement refinement. MTR achieves state-of-the-art performance on both the marginal and joint motion prediction challenges.
  arXiv  Detail & Related papers  (2022-09-27T16:23:14Z)
• Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking [32.32109475782992]
  We show that a simple motion model can obtain state-of-the-art tracking performance without other cues like appearance. We thus name the proposed method as Observation-Centric SORT, OC-SORT for short.
  arXiv  Detail & Related papers  (2022-03-27T17:57:08Z)
• MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions [70.30211294212603]
  This paper tackles video prediction from a new dimension of predicting spacetime-varying motions that are incessantly across both space and time. We propose the MotionRNN framework, which can capture the complex variations within motions and adapt to spacetime-varying scenarios.
  arXiv  Detail & Related papers  (2021-03-03T08:11:50Z)
• Object Tracking Using Spatio-Temporal Future Prediction [41.33609264685531]
  We introduce a learning-based tracking method that takes into account background motion modeling and trajectory prediction. Our trajectory prediction module predicts the target object's locations in the current and future frames based on the object's past trajectory. To dynamically switch between the appearance-based tracker and the trajectory prediction, we employ a network that can assess how good a tracking prediction is.
  arXiv  Detail & Related papers  (2020-10-15T09:02:50Z)

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.