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Multiple Object Tracking

26 papers with code · Computer Vision
Subtask of Video · Object Tracking

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

Benchmarks

Latest papers without code

Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos with Bidirectional Temporal Deep Learning Tracking Algorithm

26 Nov 2020

Compared to manual blood cell counting, CycleTrack achieves 96. 58 $\pm$ 2. 43% cell counting accuracy among 8 test videos with 1000 frames each compared to 93. 45% and 77. 02% accuracy for independent CenterTrack and SORT almost without additional time expense.

MULTIPLE OBJECT TRACKING

GMOT-40: A Benchmark for Generic Multiple Object Tracking

24 Nov 2020

Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years.

MULTIPLE OBJECT TRACKING

MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking

15 Oct 2020

We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods.

MULTIPLE OBJECT TRACKING MULTIPLE PEOPLE TRACKING ROBOT NAVIGATION SELF-DRIVING CARS

Appearance-free Tripartite Matching for Multiple Object Tracking

9 Aug 2020

Multiple Object Tracking (MOT) detects the trajectories of multiple objects given an input video, and it has become more and more popular in various research and industry areas, such as cell tracking for biomedical research and human tracking in video surveillance.

MULTIPLE OBJECT TRACKING

Dense Scene Multiple Object Tracking with Box-Plane Matching

30 Jul 2020

Multiple Object Tracking (MOT) is an important task in computer vision.

MULTIPLE OBJECT TRACKING

Learning to associate detections for real-time multiple object tracking

12 Jul 2020

With the recent advances in the object detection research field, tracking-by-detection has become the leading paradigm adopted by multi-object tracking algorithms.

MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING OBJECT DETECTION

ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation

7 Jul 2020

We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement.

MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING

IA-MOT: Instance-Aware Multi-Object Tracking with Motion Consistency

24 Jun 2020

Meanwhile, the spatial attention, which focuses on the foreground within the bounding boxes, is generated from the given instance masks and applied to the extracted embedding features.

MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING

MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method

19 May 2020

Most modern Multi-Object Tracking (MOT) systems typically apply REID-based paradigm to hold a balance between computational efficiency and performance.

MULTI-OBJECT TRACKING MULTIPLE OBJECT TRACKING

Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object tracking

22 Apr 2020

Some challenging problems in tracking multiple objects include the time-dependent cardinality, unordered measurements and object parameter labeling.

MULTIPLE OBJECT TRACKING