Multiple Object Tracking

112 papers with code • 8 benchmarks • 16 datasets

Multiple Object Tracking is the problem of automatically identifying multiple objects in a video and representing them as a set of trajectories with high accuracy.

Source: SOT for MOT

Libraries

Use these libraries to find Multiple Object Tracking models and implementations

Latest papers with no code

FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow

no code yet • 14 Aug 2023

Given the extracted flow, the flow-guided feature augmentation is designed to augment the object detection feature based on its optical flow, which improves the detection of small objects.

MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion

no code yet • 29 Jun 2023

Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception.

UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation

no code yet • 16 Jun 2023

Then, a new cross-domain MOT adaptation from existing datasets is proposed without any pre-defined human knowledge in understanding and modeling objects.

Tracking Objects with 3D Representation from Videos

no code yet • 8 Jun 2023

In this paper, we rethink the data association in 2D MOT and utilize the 3D object representation to separate each object in the feature space.

MotionTrack: Learning Motion Predictor for Multiple Object Tracking

no code yet • 5 Jun 2023

This challenge arises from two main factors: the insufficient discriminability of ReID features and the predominant utilization of linear motion models in MOT.

Z-GMOT: Zero-shot Generic Multiple Object Tracking

no code yet • 28 May 2023

In this paper, we introduce a novel approach to address the limitations of existing MOT and GMOT methods.

Linear Object Detection in Document Images using Multiple Object Tracking

no code yet • 26 May 2023

Linear objects convey substantial information about document structure, but are challenging to detect accurately because of degradation (curved, erased) or decoration (doubled, dashed).

Type-to-Track: Retrieve Any Object via Prompt-based Tracking

no code yet • NeurIPS 2023

This paper introduces a novel paradigm for Multiple Object Tracking called Type-to-Track, which allows users to track objects in videos by typing natural language descriptions.

S$^3$Track: Self-supervised Tracking with Soft Assignment Flow

no code yet • 17 May 2023

With this training approach in hand, we develop an appearance-based model for learning instance-aware object features used to construct a cost matrix based on the pairwise distances between the object features.

OVTrack: Open-Vocabulary Multiple Object Tracking

no code yet • CVPR 2023

This leaves contemporary MOT methods limited to a small set of pre-defined object categories.