DeepAI AI Chat
Log In Sign Up

Multiple Hypothesis Hypergraph Tracking for Posture Identification in Embryonic Caenorhabditis elegans

11/11/2021
by   Andrew Lauziere, et al.
University of Maryland
National Institutes of Health
0

Current methods in multiple object tracking (MOT) rely on independent object trajectories undergoing predictable motion to effectively track large numbers of objects. Adversarial conditions such as volatile object motion and imperfect detections create a challenging tracking landscape in which established methods may yield inadequate results. Multiple hypothesis hypergraph tracking (MHHT) is developed to perform MOT among interdependent objects amid noisy detections. The method extends traditional multiple hypothesis tracking (MHT) via hypergraphs to model correlated object motion, allowing for robust tracking in challenging scenarios. MHHT is applied to perform seam cell tracking during late-stage embryogenesis in embryonic C. elegans.

READ FULL TEXT
03/13/2019

Tracking without bells and whistles

The problem of tracking multiple objects in a video sequence poses sever...
12/10/2018

Learning Non-Uniform Hypergraph for Multi-Object Tracking

The majority of Multi-Object Tracking (MOT) algorithms based on the trac...
12/26/2012

Efficient Multiple Object Tracking Using Mutually Repulsive Active Membranes

Studies of social and group behavior in interacting organisms require hi...
05/02/2020

Derivation of a Constant Velocity Motion Model for Visual Tracking

Motion models play a great role in visual tracking applications for pred...
08/09/2020

Appearance-free Tripartite Matching for Multiple Object Tracking

Multiple Object Tracking (MOT) detects the trajectories of multiple obje...
04/16/2021

Drowned out by the noise: Evidence for Tracking-free Motion Prediction

Autonomous driving consists of a multitude of interacting modules, where...