Large-Scale Transformer-Based Multi-Target Tracking
| dc.contributor.author | Spjuth, Oliver | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för matematiska vetenskaper | sv |
| dc.contributor.examiner | Andersson, Adam | |
| dc.contributor.supervisor | Andersson, Adam | |
| dc.contributor.supervisor | Svedung Wettervik, Benjamin | |
| dc.date.accessioned | 2025-01-16T09:11:18Z | |
| dc.date.available | 2025-01-16T09:11:18Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | ||
| dc.description.abstract | In military surveillance, radar-based tracking of objects is essential. The growing use of small-scale drones, as seen in the Russia-Ukraine war, necessitates tracking at low speeds. At these speeds, birds are also detected and the number of false detections increases, making the already complex Multi-Target Tracking (MTT) problem more challenging. Recent advances in machine learning, particularly the transformer ar- chitecture, present new opportunities to address these challenges, making it valuable to explore their application in air surveillance contexts. Although transformers have shown promise in related fields such as automotive radar, adapting them to air surveillance presents specific hurdles. These include managing the quadratic scaling of attention as the number of detections increases, ensuring accurate state estimation across large continuous areas, and simultaneously estimating a large number of targets. To address these challenges, a four-module pipeline was developed. The first module reduced attention complexity by generating local contexts of detections for paral- lel processing. This was followed by a transformer-encoder-based filter designed to eliminate false detections (FDF). Next, the original problem was partitioned into independent subproblems using a graph-based clustering approach. One suggested implementation utilized the attention scores from the FDF to construct edges be- tween detections (nodes). The Leiden algorithm, a community detection algorithm, was then applied to identify clusters of related detections. These clusters were sub- sequently processed in parallel by the final transformer-based MTT module. This approach significantly reduced the initial memory demands of attention from approximately 320 GB to 1.6 GB while maintaining performance across the pipeline. The false detection filter achieved a balanced accuracy and F1 score of 99%, ef- fectively reducing the problem complexity. The attention-score-based partitioning method accurately identified subproblems that were predominantly optimal (single- target) or near-optimal. When evaluated using MTT metrics, the pipeline employing the attention-score- based partitioning method demonstrated promising results, with few missed or false detections and a total inference time of approximately 0.5 seconds for over 100,000 detections. The system scaled effectively with increased complexity and adapted well to varying conditions. | |
| dc.identifier.coursecode | MVEX03 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/309088 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | transformer, multiple target tracking, data association, leiden algorithm, clustering, attention, radar tracking, radar, graph clustering | |
| dc.title | Large-Scale Transformer-Based Multi-Target Tracking | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Engineering mathematics and computational science (MPENM), MSc |