Summary of Data-driven Approaches For Modelling Target Behaviour, by Isabel Schlangen et al.
Data-Driven Approaches for Modelling Target Behaviour
by Isabel Schlangen, André Brandenburger, Mengwei Sun, James R. Hopgood
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a comparative study of three machine learning-based approaches to describe the underlying object motion, which is crucial for tracking algorithms. The methods include Gaussian Processes (GPs), Interacting Multiple Model (IMM) filters, and Long Short-Term Memory (LSTM) networks. These approaches are compared against an Extended Kalman Filter (EKF) with an analytic motion model as a benchmark in simulated and real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares three machine learning methods for tracking object motion: Gaussian Processes, Interacting Multiple Model filters, and Long Short-Term Memory networks. The goal is to see which method works best when the true dynamics are unknown or too complex. The study uses simulations and real-world data to test each method against an Extended Kalman Filter with a known motion model. |
Keywords
» Artificial intelligence » Lstm » Machine learning » Tracking
