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.
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 Networks" in 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