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학술지 Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-object Tracking
Cited 42 time in scopus
저자
배승환, 윤국진
발행일
201803
출처
IEEE Transactions on Pattern Analysis and Machine Intelligence, v.40 no.3, pp.595-610
ISSN
0162-8828
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TPAMI.2017.2691769
협약과제
17HS3600, (1세부) 실시간 대규모 영상 데이터 이해·예측을 위한 고성능 비주얼 디스커버리 플랫폼 개발, 박종열
초록
© 1979-2012 IEEE. Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-Trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-The-Arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.
키워드
confidence-based data association, deep appearance learning, Multi-object tracking, online transfer learning, surveillance system, tracking-by-detection, tracklet confidence
KSP 제안 키워드
Appearance variations, Data association, Deep model, Learning methods, Multiple objects, Online multi-object tracking, Online transfer learning, Public Datasets, Surveillance system, Tracking method, Tracking-by-Detection