Mr. Dylan Spagnuolo Profile

Dylan Spagnuolo

at Lockheed Martin Corp

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Proceedings Article | 12 September 2021 Presentation + Paper

Multi-object tracking with deep learning ensemble for unmanned aerial system applications

Wanlin Xie, Jaime Ide, Daniel Izadi, Sean Banger, Thayne Walker, Ryan Ceresani, Dylan Spagnuolo, Christopher Guagliano, Henry Diaz, Jason Twedt

Proceedings Volume 11870, 118700I (2021) https://doi.org/10.1117/12.2600209

KEYWORDS: Visualization, Kinematics, Visual process modeling, Motion models, Video, Data modeling, Sensors, Optical tracking, Filtering (signal processing), Cameras

Read Abstract +

Multi-object tracking (MOT) is a crucial component of situational awareness in military defense applications. With the growing use of unmanned aerial systems (UASs), MOT methods for aerial surveillance is in high demand. Application of MOT in UAS presents specific challenges such as moving sensor, changing zoom levels, dynamic background, illumination changes, obscurations and small objects. In this work, we present a robust object tracking architecture aimed to accommodate for the noise in real-time situations. Our work is based on the tracking-by-detection paradigm where an independent object detector is first applied to isolate all potential detections and an object tracking model is applied afterwards to link unique objects between frames. Object trajectories are constructed using multiple hypothesis tracking (MHT) framework that produces the best hypothesis based on the kinematic and visual scorings. We propose a kinematic prediction model, called Deep Extended Kalman Filter (DeepEKF), in which a sequence-to-sequence architecture is used to predict entity trajectories in latent space. DeepEKF utilizes a learned image embedding along with an attention mechanism trained to weight the importance of areas in an image to predict future states. For the visual scoring, we experiment with different similarity measures to calculate distance based on entity appearances, including a convolutional neural network (CNN) encoder, pre-trained using Siamese networks. In initial evaluation experiments, we show that our method, combining scoring structure of the kinematic and visual models within a MHT framework, has improved performance especially in edge cases where entity motion is unpredictable, or the data presents frames with significant gaps.

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