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Online Multi-Object Tracking

The goal of Online Multi-Object Tracking is to estimate the spatio-temporal trajectories of multiple objects in an online video stream (i.e., the video is provided frame-by-frame), which is a fundamental problem for numerous real-time applications, such as video surveillance, autonomous driving, and robot navigation.

Source: A Hybrid Data Association Framework for Robust Online Multi-Object Tracking

Papers

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Showing 51–56 of 56 papers

Title	Date	Tasks	Status
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification	Sep 12, 2018	Large-Scale Person Re-IdentificationMulti-Object Tracking	CodeCode Available
Online multi-object tracking via robust collaborative model and sample selection	Jan 1, 2017	Multi-Object TrackingObject	CodeCode Available
FANTrack: 3D Multi-Object Tracking with Feature Association Network	May 7, 2019	3D Multi-Object TrackingMulti-Object Tracking	CodeCode Available
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers	Sep 10, 2018	Multi-Object TrackingObject Tracking	CodeCode Available
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking	Feb 26, 2018	Multi-Object TrackingMultiple Object Tracking	CodeCode Available
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs	Feb 23, 2018	Autonomous VehiclesMulti-Object Tracking	CodeCode Available

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