| dc.creator | Jorquera, Felipe | |
| dc.creator | Hernández, Sergio | |
| dc.creator | Vergara, Diego | |
| dc.date | 2022-12-26T13:13:34Z | |
| dc.date | 2022-12-26T13:13:34Z | |
| dc.date | 2018 | |
| dc.date.accessioned | 2024-05-02T20:30:09Z | |
| dc.date.available | 2024-05-02T20:30:09Z | |
| dc.identifier | http://repositorio.ucm.cl/handle/ucm/4268 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9274521 | |
| dc.description | Multi Target Tracking has many applications such as video surveillance and event recognition among others. In this paper, we present a multi object tracking (MOT) method based on point processes and random finite sets theory. The Probability Hypothesis Density (PHD) filter is a MOT algorithm that deals with missed, false and redundant detections. However, the PHD filter, as well as other conventional tracking-by-detection approaches, requires some sort of pre-processing technique such as non-maximum suppression (NMS) to eliminate redundant detections. In this paper, we show that using NMS is sub-optimal and therefore propose Determinantal Point Processes (DPP) to select the final set of detections based on quality and similarity terms. We conclude that PHD filter-DPP method outperforms PHD filter-NMS. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | Lecture Notes in Computer Science, 10657, 323-330 | |
| dc.subject | Multi object tracking | |
| dc.subject | Tracking by detection | |
| dc.subject | Determinantal Point Processes | |
| dc.title | Multi target tracking using determinantal point processes | |
| dc.type | Article | |
