Multi-Vehicle Tracking Using Heterogeneous Neural Networks for Appearance And Motion Features
DC Field | Value | Language |
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dc.contributor.author | Abdallah, Mohamed S. | - |
dc.contributor.author | Han, Da Sol | - |
dc.contributor.author | Kim, HyungWon | - |
dc.date.accessioned | 2022-11-22T02:40:12Z | - |
dc.date.available | 2022-11-22T02:40:12Z | - |
dc.date.created | 2022-09-22 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 1348-8503 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86131 | - |
dc.description.abstract | This paper presents a multi-vehicle tracking algorithm using appearance feature and motion history based on heterogeneous deep learning aimed at autonomous driving applications. Our proposed multi-vehicle tracking model follows the tracking-by-detection paradigm. To track multiple vehicles, we utilize the appearance and motion features of the target vehicles in consecutive frames. The proposed multi-vehicle tracking system employs a deep convolutional neural network, which is trained with a triplet loss minimization method to extract appearance features. The key contribution of the proposed method lies in a Long Short-Term Memory (LSTM) with a fully connected layer that accurately predicts the probability distribution of the next appearance and motion features of tracked objects. We constructed a multi-vehicle tracking dataset from various real road traffic using a camera sensor on a vehicle. To evaluate our proposed algorithm, we use several multi-target tracking datasets from the KITTI object tracking benchmark, which is a Public tracking dataset, as well as our evaluation dataset. Experimental results demonstrate that the proposed multi-vehicle tracking algorithm achieves a MOTA of 84.5% and MOTP 86.3% on the KITTI tracking dataset, and a MOTA of 81.8% and MOTP 84.8% on our evaluation dataset, an improvement of 8.6% and 9.6% over the previous methods. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH | - |
dc.title | Multi-Vehicle Tracking Using Heterogeneous Neural Networks for Appearance And Motion Features | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000838545400001 | - |
dc.identifier.doi | 10.1007/s13177-022-00320-6 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH, v.20, no.3, pp.720 - 733 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85135834940 | - |
dc.citation.endPage | 733 | - |
dc.citation.startPage | 720 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH | - |
dc.citation.volume | 20 | - |
dc.citation.number | 3 | - |
dc.contributor.affiliatedAuthor | Abdallah, Mohamed S. | - |
dc.type.docType | Article; Early Access | - |
dc.subject.keywordAuthor | Convolution Neural network (CNN) | - |
dc.subject.keywordAuthor | KL divergence | - |
dc.subject.keywordAuthor | Multiple Object Tracking (MOT) | - |
dc.subject.keywordAuthor | Object detection | - |
dc.subject.keywordAuthor | Triplet loss | - |
dc.subject.keywordAuthor | Vehicle tracking | - |
dc.relation.journalResearchArea | Transportation | - |
dc.relation.journalWebOfScienceCategory | Transportation Science & Technology | - |
dc.description.journalRegisteredClass | scopus | - |
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