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Volume: 30 | Article ID: art00017
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Multiple pedestrian tracking in moving vehicle using online learning of random ferns and feature descriptor of pre-trained shallow Convolutional Neural Networks
  DOI :  10.2352/ISSN.2470-1173.2018.17.AVM-347  Published OnlineJanuary 2018
Abstract

In this paper, we introduce a multi-pedestrian tracking algorithm for tracking from a moving vehicle. The method is based on online learning of a random ferns (RF) tracker model using the output features of a convolutional neural network (CNN). For real-time application in vehicles, an online method is applied within the tracking-by-detection framework where data association between detections and trackers is conducted online. To predict the tracker's position, we perform particle filtering with tracker models inferred from a shallow CNN. In this study, You Only Look Once (YOLO), a real-time object detection system, was adopted as the pre-trained model. Although YOLO has an accurate network for object classification, it is not appropriate for real-time multi-pedestrian tracking. Therefore, we use modified YOLO to obtain a shallow version (S-YOLO) having fewer convolutional layers and fewer filters in these layers. To update the tracker in every frame, positive and negative samples are applied to the S-YOLO and retraining is performed. Then, we extract feature descriptors from the first fully connected layer of S-YOLO to train the RF tracker models. The proposed algorithm was successfully applied to various pedestrian video sequences and yielded a more accurate tracking performance than other existing method.

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SangJun Kim, JaeYeal Nam, ByoungChul Ko, "Multiple pedestrian tracking in moving vehicle using online learning of random ferns and feature descriptor of pre-trained shallow Convolutional Neural Networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines,  2018,  pp 347-1 - 347-4,  https://doi.org/10.2352/ISSN.2470-1173.2018.17.AVM-347

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Copyright © Society for Imaging Science and Technology 2018
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