← Back to all news
arXiv Computer Vision

Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking

The paper introduces a Motion Dynamics Kalman Filter (MD‑KF) for 3D multi‑object tracking that adapts to motion variation while keeping a single‑model structure. It improves state estimation by scaling motion components to reduce drift, especially for distant or occluded objects. Experiments on KITTI, Waymo Open Dataset, and real vehicle tests show MD‑KF matches multimodel KF performance with a 1.72× speedup.

Related stories

arXiv AI
Sep 10

A Multi-Modal Perception Pipeline for Object Detection and Tracking in Autonomous Racing

The paper introduces a multi‑modal late‑fusion perception pipeline for object detection and tracking in autonomous racing. It combines independent detections from cameras, LiDARs, and RADARs to produce timely and robust state estimates of surrounding vehicles. The tracking framework compensates for detection delays and incorporates vehicle dynamics and track layout knowledge, and its effectiveness is confirmed through real‑world experiments in diverse critical scenarios.

By Davide Malvezzi, Michele Pestarino, Vittoria Cavicchioli, Valentina La Gamba, Silvia Severi, Fabio Bagni, Luca Bartoli, Massimiliano Bosi, Francesco Gatti, Micaela Verucchi, Ayoub Raji, Marko Bertogna
agentscomputer-visionroboticsmultimodalsafety
More like this →
arXiv AI
Sep 15

IMM-based Multiple Object Tracking using a State Prediction Neural Network

The paper introduces PR-IMM, a data‑driven state predictor‑based Interacting Multiple Model tracking method that integrates a transformer‑based prediction model with radar Doppler measurements. PR-IMM combines this prediction model with conventional CV, CA, and CT motion models, dynamically weighting their prior positions according to mode probabilities. Experiments demonstrate that PR-IMM reduces position‑estimation error by 57.3% compared to the standard IMM and by 16.5% compared to the prediction model alone, while also lowering ID switches by 25.3% and improving IDF1 by 9.6%.

By Chan-Bin Lim, Dong-Hee Paek, Seung-Hyun Kong
llmsagentssafety
More like this →
arXiv Computer Vision
Sep 3

TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views

TAPVid-MV is a new benchmark for tracking any point in 3D across multiple synchronized camera views. It comprises 284 sequences, 1,142 calibrated camera streams, and 109,769 point tracks, covering indoor and outdoor domains and derived from various modalities such as depth, LiDAR, SLAM, and simulation. The dataset is visually verified, and evaluation shows that current multi‑view trackers do not consistently outperform monocular trackers, highlighting geometry recovery as a key bottleneck.

By Skanda Koppula, Frano Rajic, Abdullah Faiz Ur Rahman, Yi Yang, Ignacio Rocco, Jeet Thakwani, Rishabh Kabra, Andrew Zisserman, Joao Carreira, Siyu Tang, Carl Doersch, Gabriel Brostow
agentsroboticsbenchmarks
More like this →
arXiv Computer Vision
Sep 7

Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods

The paper introduces MovingDroneCrowd++, a large-scale video dataset for dense crowd counting and tracking from moving drones, featuring varied flight altitudes, camera angles, and lighting. It presents two new methods: GD3A for Video Individual Counting and GIA-Track for Multi-Object Tracking, both leveraging group-wise density assignment and identity association to handle aerial challenges. Experiments demonstrate significant improvements, reducing counting error by 47.4% and boosting tracking accuracy by 64.6%.

By Yaowu Fan, Jia Wan, Tao Han, Andy J. Ma, Wanli Ouyang, Antoni B. Chan
roboticsbenchmarks
More like this →
arXiv Computer Vision
Sep 16

Racing in Volume with Flow Ensembles

The paper introduces FastFlowGS, a streaming 4D Gaussian Splatting method that reconstructs fast-moving subjects from a sparse set of external cameras, and Monaco4D, a photorealistic Unreal Engine 5 benchmark featuring Formula 1 sequences with dense ground truth. FastFlowGS combines sparse matches, semi-dense tracks, and dense optical flow using a Kalman-style temporal update, achieving significant performance gains over existing baselines on both CMU-Panoptic and Monaco4D datasets. The benchmark provides varied illumination and viewpoints from trackside, onboard, and drone cameras, enabling evaluation of high-speed outdoor reconstruction.

By Saswat Subhajyoti Mallick, Riu Cherdchusakulchai, Marc Ruiz Olle, Albert Mosella-Montoro, Jose Ribeiro-Gomes, Francisco Vicente Carrasco, Fernando De la Torre
computer-visionroboticsbenchmarks
More like this →