Papers
1
Total Citations
3
H-Index
1
About
Seoyeon Jang is a rising researcher in robotics and computer vision, with a focus on real-time perception for autonomous systems. Her work centers on dynamic scene understanding, particularly the challenge of simultaneously tracking moving objects and segmenting them from static environments—a critical problem for safe navigation in self-driving cars and mobile robots. Her most-cited paper, "TOSS: Real-Time Tracking and Moving Object Segmentation for Static Scene Mapping" (2024), introduces an innovative framework that integrates object tracking with motion segmentation to build accurate, static maps in dynamic settings. This contribution addresses a key bottleneck in visual SLAM, enabling robots to distinguish between stationary and moving elements in real time. Though early in her career, Jang’s work has already garnered attention, with 3 citations for this foundational paper, signaling its relevance to the field. Her research promises to advance robust perception in cluttered, unpredictable environments, making her a notable emerging voice in the intersection of tracking, segmentation, and mapping.
Research Focus
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Top Papers
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