Rao-Blackwellized Monte Carlo Data Association With Deep Metric For Object Tracking

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

We propose a deep Rao-Blackwellized Monte Carlo data association particle filter (DeepRBMCDA) which is a modification of the existing RBMCDA using Hungarian association. It uses YOLOv7 detected bounding box with deep ReIdentification (ReID) descriptors to track detected objects in Bayesian way. In our work, we demonstrate our performance on a diverse GMOT-40 dataset which contains sequences of varying class objects of similar appearance. We evaluate our tracker and compare its performance with state-of-the-art trackers. We obtain comparable multi object tracking accuracy (MOTA), multi object tracking precision (MOTP), localization accuracy (LocA), and multi object detection accuracy (MODA), improved mostly tracked (MT), reduced mostly lost (ML), and lowest fragmentation (Frag). We also perform the ablation study which reports highest higher order tracking accuracy (HOTA), HOTA combined LocA (HOTALocA), MOTA, identity switching (IDSW), MT, ML, Frag, and identity based F1 score (IDFI) on tracking ground-truth labels. Using particle filter for object tracking provides robustness which can be helpful in diverse dynamic tracking scenarios.
Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing, MLSP 2023
EditorsDanilo Comminiello, Michele Scarpiniti
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)979-8-3503-2411-2
ISBN (Print)979-8-3503-2412-9
DOIs
Publication statusPublished - 23 Oct 2023
MoE publication typeA4 Conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Rome, Italy
Duration: 17 Sept 202320 Sept 2023

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
ISSN (Electronic)2161-0371

Conference

ConferenceIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
Country/TerritoryItaly
CityRome
Period17/09/202320/09/2023

Keywords

  • multi object tracking
  • DeepRBMCDA
  • MOT
  • Rao-Blackwellization
  • Particle filter
  • Kalman filtering
  • YOLO detection
  • deep features

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