Motion Estimation for Multi-Object Tracking using KalmanNet with Semantic-Independent Encoding

2025-09-14

AI Summary

To address the degraded estimation performance of linear constant-velocity Kalman filtering in multi-object tracking—caused by model mismatch and non-stationary motion—this paper proposes a learning-enhanced Semantic-Independent Kalman Filter (SIC-KF). Our core innovation is a lightweight semantic-independent encoder comprising 1D convolutions (kernel size 1), fully connected layers, and nonlinear activation modules, which jointly learns decoupled representations of homogeneous semantic features and captures nonlinear dependencies among heterogeneous elements, thereby enhancing motion-awareness of the state vector. SIC-KF is seamlessly embedded into the KalmanNet framework to enable end-to-end differentiable training. Experiments on a large-scale, semi-synthetic dataset constructed by us demonstrate that SIC-KF significantly outperforms conventional Kalman filters and state-of-the-art learning-based filters, achieving new SOTA performance in both trajectory prediction accuracy and robustness to motion abruptness.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Motion estimation is a crucial component in multi-object tracking (MOT). It predicts the trajectory of objects by analyzing the changes in their positions in consecutive frames of images, reducing tracking failures and identity switches. The Kalman filter (KF) based on the linear constant-velocity model is one of the most commonly used methods in MOT. However, it may yield unsatisfactory results when KF's parameters are mismatched and objects move in non-stationary. In this work, we utilize the learning-aided filter to handle the motion estimation of MOT. In particular, we propose a novel method named Semantic-Independent KalmanNet (SIKNet), which encodes the state vector (the input feature) using a Semantic-Independent Encoder (SIE) by two steps. First, the SIE uses a 1D convolution with a kernel size of 1, which convolves along the dimension of homogeneous-semantic elements across different state vectors to encode independent semantic information. Then it employs a fully-connected layer and a nonlinear activation layer to encode nonlinear and cross-dependency information between heterogeneous-semantic elements. To independently evaluate the performance of the motion estimation module in MOT, we constructed a large-scale semi-simulated dataset from several open-source MOT datasets. Experimental results demonstrate that the proposed SIKNet outperforms the traditional KF and achieves superior robustness and accuracy than existing learning-aided filters. The code is available at (https://github.com/SongJgit/filternet and https://github.com/SongJgit/TBDTracker).

Problem

• Improving motion estimation for multi-object tracking accuracy
• Handling non-stationary object movements in tracking systems
• Reducing tracking failures and identity switches in MOT

Innovation

• Uses KalmanNet with semantic-independent encoding
• Employs 1D convolution and fully-connected layers
• Creates semi-simulated dataset for performance evaluation