Multi-Object Tracking and Segmentation

16 papers with code • 2 benchmarks • 3 datasets

Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes.

(Image and definition credit: Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation, NeurIPS 2021, Spotlight )

Most implemented papers

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

bdd100k/bdd100k CVPR 2020

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.

EagerMOT: 3D Multi-Object Tracking via Sensor Fusion

aleksandrkim61/EagerMOT 29 Apr 2021

Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time.

D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos

schmiddo/d2conv3d WACV 2021

We further show that D2Conv3D out-performs trivial extensions of existing dilated and deformable convolutions to 3D.

Segment as Points for Efficient Online Multi-Object Tracking and Segmentation

detectRecog/PointTrack ECCV 2020

The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods including 3D tracking methods by large margins (5. 4% higher MOTSA and 18 times faster over MOTSFusion) with the near real-time speed (22 FPS).

PointTrack++ for Effective Online Multi-Object Tracking and Segmentation

detectRecog/PointTrack 3 Jul 2020

In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.

Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion

SonginCV/MAF_HDA 31 Aug 2020

One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter.

Continuous Copy-Paste for One-Stage Multi-Object Tracking and Segmentation

detectrecog/ccp ICCV 2021

Current one-step multi-object tracking and segmentation (MOTS) methods lag behind recent two-step methods.

Assignment-Space-Based Multi-Object Tracking and Segmentation

AnwesaChoudhuri/AssignmentSpace-MOTS ICCV 2021

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.

Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation

SysCV/pcan NeurIPS 2021

We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich spatio-temporal information for online multiple object tracking and segmentation.

Do Different Tracking Tasks Require Different Appearance Models?

Zhongdao/UniTrack NeurIPS 2021

We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered.