Integration of Regularized l1 Tracking and Instance Segmentation for Video Object Tracking

• URL: http://arxiv.org/abs/1912.12883v1
• Date: Mon, 30 Dec 2019 11:14:14 GMT
• Title: Integration of Regularized l1 Tracking and Instance Segmentation for Video Object Tracking
• Authors: Filiz Gurkan and Bilge Gunsel
• Abstract summary: We introduce a tracking-by-detection method that integrates a deep object detector with a particle filter tracker. A novel observation model which establishes consensus between the detector and tracker is formulated. We propose a new state vector consisting of translation, rotation, scaling and shearing parameters that allows tracking the deformed object bounding boxes.
• Score: 1.90365714903665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a tracking-by-detection method that integrates a deep object detector with a particle filter tracker under the regularization framework where the tracked object is represented by a sparse dictionary. A novel observation model which establishes consensus between the detector and tracker is formulated that enables us to update the dictionary with the guidance of the deep detector. This yields an efficient representation of the object appearance through the video sequence hence improves robustness to occlusion and pose changes. Moreover we propose a new state vector consisting of translation, rotation, scaling and shearing parameters that allows tracking the deformed object bounding boxes hence significantly increases robustness to scale changes. Numerical results reported on challenging VOT2016 and VOT2018 benchmarking data sets demonstrate that the introduced tracker, L1DPF-M, achieves comparable robustness on both data sets while it outperforms state-of-the-art trackers on both data sets where the improvement achieved in success rate at IoU-th=0.5 is 11% and 9%, respectively.

Related papers

• SeMoLi: What Moves Together Belongs Together [51.72754014130369]
  We tackle semi-supervised object detection based on motion cues. Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects. We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
  arXiv  Detail & Related papers  (2024-02-29T18:54:53Z)
• SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth [84.64121608109087]
  We propose a pseudo-depth estimation method for obtaining the relative depth of targets from 2D images. Secondly, we design a depth cascading matching (DCM) algorithm, which can use the obtained depth information to convert a dense target set into multiple sparse target subsets. By integrating the pseudo-depth method and the DCM strategy into the data association process, we propose a new tracker, called SparseTrack.
  arXiv  Detail & Related papers  (2023-06-08T14:36:10Z)
• SCTracker: Multi-object tracking with shape and confidence constraints [11.210661553388615]
  This paper proposes a multi-object tracker based on shape constraint and confidence named SCTracker. Intersection of Union distance with shape constraints is applied to calculate the cost matrix between tracks and detections. The Kalman Filter based on the detection confidence is used to update the motion state to improve the tracking performance when the detection has low confidence.
  arXiv  Detail & Related papers  (2023-05-16T15:18:42Z)
• You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking [9.20064374262956]
  The proposed framework can achieve robust tracking by using only a 2D detector and a 3D detector. It is proven more accurate than many of the state-of-the-art TBD-based multi-modal tracking methods.
  arXiv  Detail & Related papers  (2023-04-18T02:45:18Z)
• End-to-end Tracking with a Multi-query Transformer [96.13468602635082]
  Multiple-object tracking (MOT) is a challenging task that requires simultaneous reasoning about location, appearance, and identity of the objects in the scene over time. Our aim in this paper is to move beyond tracking-by-detection approaches, to class-agnostic tracking that performs well also for unknown object classes.
  arXiv  Detail & Related papers  (2022-10-26T10:19:37Z)
• Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking [59.79252390626194]
  We propose a novel solution named TransSTAM, which leverages Transformer to model both the appearance features of each object and the spatial-temporal relationships among objects. The proposed method is evaluated on multiple public benchmarks including MOT16, MOT17, and MOT20, and it achieves a clear performance improvement in both IDF1 and HOTA.
  arXiv  Detail & Related papers  (2022-05-31T01:19:18Z)
• Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking [82.34356879078955]
  We propose a compact memory embedding to enhance the discrimination of the segmentation-based deformable visual tracking method. Our method outperforms the excellent segmentation-based trackers, i.e., D3S and SiamMask on DAVIS 2017 benchmark.
  arXiv  Detail & Related papers  (2021-11-23T03:07:12Z)
• DeepScale: An Online Frame Size Adaptation Framework to Accelerate Visual Multi-object Tracking [8.878656943106934]
  DeepScale is a model agnostic frame size selection approach to accelerate tracking throughput. It can find a suitable trade-off between tracking accuracy and speed by adapting frame sizes at run time. Compared to a state-of-the-art tracker, DeepScale++, a variant of DeepScale achieves 1.57X accelerated with only moderate degradation.
  arXiv  Detail & Related papers  (2021-07-22T00:12:58Z)
• TDIOT: Target-driven Inference for Deep Video Object Tracking [0.2457872341625575]
  In this work, we adopt 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. The proposed single object tracker, TDIOT, applies an appearance similarity-based temporal matching for data association.
  arXiv  Detail & Related papers  (2021-03-19T20:45:06Z)
• Simultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking [94.24393546459424]
  We introduce Deep Motion Modeling Network (DMM-Net) that can estimate multiple objects' motion parameters to perform joint detection and association. DMM-Net achieves PR-MOTA score of 12.80 @ 120+ fps for the popular UA-DETRAC challenge, which is better performance and orders of magnitude faster. We also contribute a synthetic large-scale public dataset Omni-MOT for vehicle tracking that provides precise ground-truth annotations.
  arXiv  Detail & Related papers  (2020-08-20T08:05:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.