TDIOT: Target-Driven Inference for Deep Video Object Tracking

Filiz Gurkan, Llukman Cerkezi, Ozgun Cirakman, Bilge Gunsel*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Recent tracking-by-detection approaches use deep object detectors as target detection baseline, because of their high performance on still images. For effective video object tracking, object detection is integrated with a data association step performed by either a custom design inference architecture or an end-to-end joint training for tracking purpose. In this work, we adopt the former approach and use the pre-trained Mask R-CNN deep object detector as the baseline. We introduce a novel inference architecture placed on top of FPN-ResNet101 backbone of Mask R-CNN to jointly perform detection and tracking, without requiring additional training for tracking purpose. The proposed single object tracker, TDIOT, applies an appearance similarity-based temporal matching for data association. To tackle tracking discontinuities, we incorporate a local search and matching module into the inference head layer that exploits SiamFC. Moreover, to improve robustness to scale changes, we introduce a scale adaptive region proposal network that enables to search for the target at an adaptively enlarged spatial neighborhood specified by the trace of the target. In order to meet long term tracking requirements, a low cost verification layer is incorporated into the inference architecture to monitor presence of the target based on its LBP histogram model. Performance evaluation on videos from VOT2016, VOT2018, and VOT-LT2018 datasets demonstrate that TDIOT achieves higher accuracy compared to the state-of-the-art short-term trackers while it provides comparable performance in long term tracking. We also compare our tracker on LaSOT dataset where we observe that TDIOT provides comparable performance with other methods that are trained on LaSOT. The source code and TDIOT output videos are accessible at https://github.com/msprITU/TDIOT.

Original languageEnglish
Pages (from-to)7938-7951
Number of pages14
JournalIEEE Transactions on Image Processing
Volume30
DOIs
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 1992-2012 IEEE.

Keywords

  • Deep object detector
  • particle sampler
  • region proposal network

Fingerprint

Dive into the research topics of 'TDIOT: Target-Driven Inference for Deep Video Object Tracking'. Together they form a unique fingerprint.

Cite this