Understanding Sensor Fusion and Tracking, Part 5: How to Track Multiple Objects at Once

Check out the other videos in the series: Part 1 - What Is Sensor Fusion?: https://youtu.be/6qV3YjFppuc Part 2 - Fusing an Accel, Mag, and Gyro to Estimation Orientation: https://youtu.be/0rlvvYgmTvI Part 3 - Fusing a GPS and IMU to Estimate Pose: https://youtu.be/hN8dL55rP5I Part 4 - Tracking a Single Object With an IMM Filter: https://youtu.be/hJG08iWlres This video describes two common problems that arise when tracking multiple objects: data association and track maintenance. We cover a few ways to solve these issues and provide a general way to approach all multi-object tracking problems. We cover data association algorithms like global nearest neighbor (GNN) and joint probabilistic data association (JPDA) and look at the criteria for deleting and creating tracks. We talk about gating observations so that we don’t waste computational resources. At the end of the video, we show an example of GNN and JPDA algorithms operating on two objects in close proximity. Check out these other references! Multi-Object Trackers: https://bit.ly/2qrpzB1 Ebook: Multi-Object Tracking for Autonomous Systems and Surveillance Systems - https://bit.ly/3fChIV1 Multi-Object Tracking: https://bit.ly/3mMPH0s -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.