(PDF) Assignment-Space-Based Multi-Object Tracking and Segmentation
Assignment-Space-Based Multi-Object Tracking and Segmentation
Assignment-Space-Based Multi-Object Tracking and Segmentation (ICCV 2021)
Figure 1 from Assignment-Space-based Multi-Object Tracking and
Figure 1 from Assignment-Space-based Multi-Object Tracking and
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COMMENTS
Assignment-Space-based Multi-Object Tracking and Segmentation
Abstract: Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tracking of objects over time remains a challenge.
Assignment-Space-Based Multi-Object Tracking and Segmentation
Assignment-Space-BasedMulti-ObjectTrackingandSegmentation. AnwesaChoudhuri, GirishChowdhary, AlexanderG. Schwing; ProceedingsoftheIEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13598-13607. Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data.
Assignment-Space-based Multi-Object Tracking and Segmentation
Assignment-Space-basedMulti-ObjectTrackingandSegmentation. This work forms a global method for MOTS over the space of assignments rather than detections and develops a structured prediction formulation to score assignment sequences across any number of consecutive frames. Expand.
Assignment-Space-based Multi-Object Tracking and Segmentation
Goal: Jointly address detection, segmentation and tracking. Assignment-Space-based Multi-ObjectTrackingandSegmentationAnwesa Choudhuri, GirishChowdhary, Alexander Schwing, University of Illinois at Urbana-Champaign
Assignment-Space MOTS
Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tracking of objects over time remains a challenge.
Assignment-Space-based Multi-Object Tracking and Segmentation
In this paper, we propose a model-free multi-objecttrackingapproach that uses a category-agnostic image segmentation method to track objects.
Supplementary Material: Assignment-Space-based Multi-Object Tracking and Segmentation. Inthissection, weprovideadditional details and anal-ysis of the proposed approach for Multi-ObjectTrackingandSegmentation. In Sec. A we elaborateon the param-eter learning procedure forMOTSthat has been discussed in Sec. 3.3.
Assignment-Space-based Multi-Object Tracking and Segmentation
Figure 1. Use of assignment space better preserves identities of objects when compared to PointTrack [58]. We highlight the identity switches from PointTrack using yellow rectangles. Our method is able to recover those identities (highlighted using cyan rectangles).
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In contrast, we formulate a global method for MOTS over the space of assignments rather than detections: First, we find all top-k assignments of objects detected and segmented between any two consecutive frames and develop a structured prediction formulation to score assignment sequences across any number of consecutive frames.
AnwesaChoudhuri/AssignmentSpace-MOTS - GitHub
Assignment-Space-BasedMulti-ObjectTrackingandSegmentation (ICCV2021) AnwesaChoudhuri, Girish Chowdhary, Alexander G. Schwing. [Publication] [Project] [BibTeX] Getting Started. Prerequisites: Virtual environment with Python 3.6. Pytorch 1.3.1. Other requirements: $ pip install -r requirements.txt. Dataset: KITTI Images + Annotations.
IMAGES
VIDEO
COMMENTS
Abstract: Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tracking of objects over time remains a challenge.
Assignment-Space-Based Multi-Object Tracking and Segmentation. Anwesa Choudhuri, Girish Chowdhary, Alexander G. Schwing; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 13598-13607. Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data.
Assignment-Space-based Multi-Object Tracking and Segmentation. This work forms a global method for MOTS over the space of assignments rather than detections and develops a structured prediction formulation to score assignment sequences across any number of consecutive frames. Expand.
Goal: Jointly address detection, segmentation and tracking. Assignment-Space-based Multi-Object Tracking and Segmentation Anwesa Choudhuri, Girish Chowdhary, Alexander Schwing, University of Illinois at Urbana-Champaign
Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tracking of objects over time remains a challenge.
In this paper, we propose a model-free multi-object tracking approach that uses a category-agnostic image segmentation method to track objects.
Supplementary Material: Assignment-Space-based Multi-Object Tracking and Segmentation. In this section, we provide additional details and anal-ysis of the proposed approach for Multi-Object Tracking and Segmentation. In Sec. A we elaborate on the param-eter learning procedure for MOTS that has been discussed in Sec. 3.3.
Figure 1. Use of assignment space better preserves identities of objects when compared to PointTrack [58]. We highlight the identity switches from PointTrack using yellow rectangles. Our method is able to recover those identities (highlighted using cyan rectangles).
In contrast, we formulate a global method for MOTS over the space of assignments rather than detections: First, we find all top-k assignments of objects detected and segmented between any two consecutive frames and develop a structured prediction formulation to score assignment sequences across any number of consecutive frames.
Assignment-Space-Based Multi-Object Tracking and Segmentation (ICCV 2021) Anwesa Choudhuri, Girish Chowdhary, Alexander G. Schwing. [Publication] [Project] [BibTeX] Getting Started. Prerequisites: Virtual environment with Python 3.6. Pytorch 1.3.1. Other requirements: $ pip install -r requirements.txt. Dataset: KITTI Images + Annotations.