Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking

• URL: http://arxiv.org/abs/2605.00362v1
• Date: Fri, 01 May 2026 02:51:59 GMT
• Title: Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking
• Abstract summary: Multi-object tracking (MOT) is critical in numerous real-world applications, including surveillance, autonomous driving, and robotics.<n>We introduce the Temporal Convolutional Motion Predictor (TCMP), a novel framework for MOT that leverages a modified Temporal Convolutional Network (TCN) featuring dilated convolutions and a regression head.<n> Experimental results demonstrate that our approach achieves state-of-the-art performance, specifically improves upon the previous best method in several key metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-object tracking (MOT) is critical in numerous real-world applications, including surveillance, autonomous driving, and robotics. Accurately predicting object motion is fundamental to MOT, but current methods struggle with the complexities of real-world, non-linear motion (e.g., sudden stops, sharp turns). While recent research has gravitated towards increasingly complex and computationally expensive generative models to tackle this problem, their practical utility is often constrained. This paper challenges that paradigm, arguing that such complexity is not only unnecessary but can be outperformed by a more efficient, purpose-built approach. We introduce the Temporal Convolutional Motion Predictor (TCMP), a novel framework for MOT that leverages a modified Temporal Convolutional Network (TCN) featuring dilated convolutions and a regression head. This design allows for effective motion prediction across arbitrary temporal context lengths. Experimental results demonstrate that our approach achieves state-of-the-art performance, specifically improves upon the previous best method in several key metrics: HOTA (a measure of overall tracking accuracy) increases from 62.3% to 63.4%, IDF1 (a measure of identity preservation) rises from 63.0% to 65.0%, and AssA (a measure of association accuracy) improves from 47.2% to 49.1%. Significantly, TCMP achieves this performance while being highly efficient; it has only 0.014 times the parameters and requires only 0.05 times the computational cost (FLOPs) compared to the SOTA method. while is only 0.014 times the size (in terms of parameters) and requires only 0.05 times the computational cost (in terms of FLOPs). These findings highlight the robustness of our method to advance MOT systems by ensuring adaptability, accuracy, and efficiency in complex tracking environments.

Related papers

• Invertible Neural Network Adapter for One-Step Flow Matching in Robot Manipulation [48.77928097573051]
  This paper presents an invertible neural network adapter for general robotic manipulation.<n>Built upon a flow-matching formulation, the proposed adapter constrains the action generation trajectory within an invertible latent space.<n>Experiments are conducted across a diverse set of simulation benchmarks and real-world robotic platforms to evaluate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2026-06-17T15:35:27Z)
• EventShiftFlow: Towards Hardware-efficient FPGA-based Flow Estimation [43.77587889970362]
  Event-based vision sensors offer asynchronous, high-temporal-resolution measurements.<n>Many event-based motion estimation methods are computationally intensive and difficult to map to FPGA hardware.<n>We present a streaming velocity estimator that discretizes asynchronous events into fixed-duration time bins, constructs a 1-bit spatial occupancy grid, and evaluates multiple velocity hypotheses in parallel.
  arXiv  Detail & Related papers  (2026-05-27T11:13:58Z)
• Just in time Informed Trees: Manipulability-Aware Asymptotically Optimized Motion Planning [46.553063931217245]
  We introduce the Just-in-Time Informed Trees (JIT*) algorithm, an enhancement over Effort Informed Trees (EIT*)<n>JIT* consistently outperforms traditional sampling-based planners across $mathbbR4$ to $mathbbR16$ dimensions.
  arXiv  Detail & Related papers  (2026-01-27T18:58:51Z)
• EfficientLLM: Efficiency in Large Language Models [64.3537131208038]
  Large Language Models (LLMs) have driven significant progress, yet their growing counts and context windows incur prohibitive compute, energy, and monetary costs.<n>We introduce EfficientLLM, a novel benchmark and the first comprehensive empirical study evaluating efficiency techniques for LLMs at scale.
  arXiv  Detail & Related papers  (2025-05-20T02:27:08Z)
• Easy-Poly: A Easy Polyhedral Framework For 3D Multi-Object Tracking [23.40561503456164]
  We present Easy-Poly, a real-time, filter-based 3D MOT framework for multiple object categories.<n>Results show that Easy-Poly outperforms state-of-the-art methods such as Poly-MOT and Fast-Poly.<n>These findings highlight Easy-Poly's adaptability and robustness in diverse scenarios.
  arXiv  Detail & Related papers  (2025-02-25T04:01:25Z)
• Fast-COS: A Fast One-Stage Object Detector Based on Reparameterized Attention Vision Transformer for Autonomous Driving [3.617580194719686]
  This paper introduces Fast-COS, a novel single-stage object detection framework crafted specifically for driving scenes.<n> RAViT achieves 81.4% Top-1 accuracy on the ImageNet-1K dataset.<n>It surpasses leading models in efficiency, delivering up to 75.9% faster GPU inference and 1.38 higher throughput on edge devices.
  arXiv  Detail & Related papers  (2025-02-11T09:54:09Z)
• OneTrack-M: A multitask approach to transformer-based MOT models [0.0]
  Multi-Object Tracking (MOT) is a critical problem in computer vision.<n>OneTrack-M is a transformer-based MOT model designed to enhance tracking computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-02-06T20:02:06Z)
• HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking [7.916733469603948]
  HybridTrack is a novel 3D multi-object tracking approach for vehicles.<n>It integrates a data-driven Kalman Filter (KF) within a tracking-by-detection paradigm.<n>It achieves 82.72% HOTA accuracy, significantly outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-01-02T14:17:19Z)
• Video Prediction Transformers without Recurrence or Convolution [65.93130697098658]
  We propose PredFormer, a framework entirely based on Gated Transformers.<n>We provide a comprehensive analysis of 3D Attention in the context of video prediction.<n>The significant improvements in both accuracy and efficiency highlight the potential of PredFormer.
  arXiv  Detail & Related papers  (2024-10-07T03:52:06Z)
• PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture [46.266960248570086]
  This study tackles the quadratic complexity of the self-attention mechanism by introducing a complexity local attention mechanism for effective feature aggregation. We also introduce a parameter-free channel temperature adaptation mechanism that adaptively adjusts the attention weight distribution in each channel. We show that PointMT achieves performance comparable to state-of-the-art methods while maintaining an optimal balance between performance and accuracy.
  arXiv  Detail & Related papers  (2024-08-10T10:16:03Z)
• SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving [27.776472262857045]
  This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles. We propose a compact and efficient global feature fusion module that performs directed message passing in a symmetric manner. As a strong baseline, SIMPL exhibits highly competitive performance on Argoverse 1 & 2 motion forecasting benchmarks.
  arXiv  Detail & Related papers  (2024-02-04T15:07:49Z)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)

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.