TDIOT: Target-Driven Inference for Deep Video Object Tracking


Gurkan F., Cerkezi L., Cirakman O., Günsel Kalyoncu B.

IEEE TRANSACTIONS ON IMAGE PROCESSING, vol.30, pp.7938-7951, 2021 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 30
  • Publication Date: 2021
  • Doi Number: 10.1109/tip.2021.3112010
  • Title of Journal : IEEE TRANSACTIONS ON IMAGE PROCESSING
  • Page Numbers: pp.7938-7951
  • Keywords: Target tracking, Detectors, Proposals, Training, Object tracking, Object detection, Measurement, Deep object detector, particle sampler, region proposal network, ROBUST VISUAL TRACKING, SCALE

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.