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Volume: 32 | Article ID: art00011
Multiple pedestrian tracking using Siamese random forests and shallow Convolutional Neural Networks
  DOI :  10.2352/ISSN.2470-1173.2020.16.AVM-108  Published OnlineJanuary 2020

In this study, we propose a new multi-pedestrian tracking (MPT) method that performs quickly and efficiently track pedestrians in real-time system. The proposed method considers combining shallow convolutional neural networks (CNN) with ensemble learning method, Siamese random forests (SRF). Unlike conventional methods, to promote robustness of ensemble method, feature transformation is applied which exploit shallow networks in appearances of still images to extract enrich features. We formulate the problem of MOT in a structured learning framework based on SRF. Each forest learns differences of random feature pairs, which are extracted from the former process to enhance robustness to easily happened circumstances in a moving vehicle. When it compares to the conventional tracking algorithms, the proposed approach, based on SRF, takes advantage of lightweight and efficiency. The proposed lightweight multiple pedestrian tracker was successfully applied to benchmark datasets and yielded a similar or better performance level as compared with state-of-theart methods.

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Jimi Lee, Jaeyeal Nam, ByoungChul Ko, "Multiple pedestrian tracking using Siamese random forests and shallow Convolutional Neural Networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines,  2020,  pp 108-1 - 108-5,

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