Simple Beats Complex: A Lightweight Time-Series Motion Predictor for Multi-Object Tracking

Forum topic · 小凯 · 2026-05-04

Summary

A forum post discusses an arXiv paper (2605.00362) arguing that complex non-linear motion in multi-object tracking (MOT) does not require complex generative models. The authors — Nhat-Tan Do, Le-Huy Tu, Nhi Ngoc-Yen Nguyen, Dieu-Phuong Nguyen, and Trong-Hop Do — propose combining time-series forecasting of historical trajectories with a lightweight, robust motion model. The approach handles sudden braking, sharp turns, lane changes, stops, and starts while remaining computationally efficient enough for real-time inference on embedded systems without expensive GPUs. The post frames this as a counter-intuitive, Occam's-razor-style finding: heavier generative models in MOT often overfit noise, degrade real-time performance, and deliver limited practical gains, whereas simple time-series methods capture motion regularities with better robustness, interpretability, and deployability. It concludes with engineering takeaways for practitioners building motion prediction or tracking systems, questioning whether added model complexity is justified.

Paper Information

Paper: Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking
Authors: Nhat-Tan Do, Le-Huy Tu, Nhi Ngoc-Yen Nguyen, Dieu-Phuong Nguyen, Trong-Hop Do
arXiv: 2605.00362 (2026-04-29)

The Tracking Dilemma: Motion Too Complex for AI?

In autonomous driving scenarios, multi-object tracking (MOT) must:

Track all vehicles and pedestrians in real time
Predict where they will go next

But real-world motion is highly complex: sudden braking, sharp turns, lane changes, stops, and starts.

Current approaches face a dilemma: increasingly complex generative models bring rising computational costs, falling real-time performance, and only limited gains in accuracy. Are these models over-engineered for the problem?

Core Finding: Simple Models Suffice for Complex Motion

The paper's central, counter-intuitive claim:

> Real-world complex non-linear motion does not need complex generative models to be modeled. Time-series methods combined with a simple motion model are enough.

The technical approach:

1. Time-series method — uses historical trajectories to predict future positions; simple but effective. 2. Lightweight motion model — not complex, but robust and computationally efficient. 3. Handles challenging motion — sudden stops, sharp turns, and non-linear trajectories. 4. Computationally efficient — runs in real time without expensive GPUs, suitable for embedded systems.

An analogy: using a complex model for this task is like delivering food with a space shuttle; the simple method is an electric scooter — more practical, more efficient, and just as good.

Why Simple Can Beat Complex

Problems with complex models:

Overfitting: fitting noise in training data, poor generalization
High computational cost: poor real-time behavior, hard to deploy, resource-hungry

Advantages of simple methods:

Robustness: no noise fitting, stronger generalization, good real-world performance
Efficiency: real-time, low compute, deployable
Interpretability: predictable behavior, easy to debug

Takeaways for Practitioners

If you build motion prediction or MOT systems, ask:

1. Is my model overly complex? 2. Would a simple method already suffice? 3. Is computational efficiency being neglected? 4. Do I truly understand the nature of the motion?

The paper's key lesson: in engineering practice, "good enough" is often more valuable than "theoretically optimal." The best tracker for autonomous driving is not the most complex one, but the most reliable one — a reminder of Occam's razor: complexity does not equal better; understanding does.