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Then, a Siamese network calculates the degree of similarity between all tracks and the new detections found at each new frame. Finally, a recurrent LSTM network is used to extract 3D and bounding box information. This model follows the tracking-by-detection paradigm and has been trained with track sequences to be able to handle missed observations and to reduce identity switches. A validation test was carried out on the Argoverse dataset to validate the performance of the proposed system. The developed deep learning approach could improve current multi-object tracking systems based on classic algorithms like the Kalman filter.<\/jats:p>","DOI":"10.3233\/ica-230702","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T16:30:56Z","timestamp":1676997056000},"page":"121-134","source":"Crossref","is-referenced-by-count":30,"title":["An improved deep learning architecture for multi-object tracking systems"],"prefix":"10.1177","volume":"30","author":[{"given":"Jes\u00fas","family":"Urdiales","sequence":"first","affiliation":[]},{"given":"David","family":"Mart\u00edn","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 Mar\u00eda","family":"Armingol","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/ICA-230702_ref1","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.1007\/978-981-16-9492-9_336","article-title":"A Tracking-By-Detection Based 3D Multiple Object Tracking for Autonomous Driving","volume":"861 LNEE","author":"Wang","year":"2022","journal-title":"Lecture Notes in Electrical Engineering"},{"key":"10.3233\/ICA-230702_ref2","first-page":"4113","article-title":"Visual tracking via online discriminative multiple instance metric learning","volume":"77","author":"Honghong","year":"2017","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/ICA-230702_ref3","doi-asserted-by":"crossref","first-page":"3029","DOI":"10.1109\/ICCV.2015.347","article-title":"Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor","author":"Choi","year":"2015","journal-title":"2015 IEEE International Conference on Computer Vision (ICCV)"},{"key":"10.3233\/ICA-230702_ref4","doi-asserted-by":"crossref","unstructured":"Xiong D, Lu H, Yu Q, Xiao J, Han W, Zheng Z. 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