Researchers from U Texas and Apple Propose a Novel Transformer-Based Architecture for Global Multi-Object Tracking

April 7, 2022

Multi-object tracking aims to locate and track all objects in a video feed. It’s a fundamental component in domains like mobile robots, where an autonomous system must navigate dynamic surroundings populated by other mobile agents. Thanks to breakthroughs in deep learning and object detection, tracking-by-detection has become the dominant tracking paradigm in recent years.

Tracking-by-detection simplifies the process by reducing it to just two steps: detection and association. First, an object detector searches each video stream frame for probable items. The second phase is an association step, which connects detections over time. Local trackers are greedy when it comes to pairwise relationships. They keep track of each trajectory’s state based on its position and/or identity traits and correlate current-frame detections with it based on its last visible status.

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