Dec 7, 2022 · We propose a novel dual-path transformation network (DTN) that decouples the shared features into detection-specific and ReID-specific representations.
Dec 7, 2022 · By learning task-specific features, this module satisfies the different requirements of both subtasks. Moreover, we observe that previous ...
By learning task-specific features, this module satisfies the different requirements of both subtasks. Moreover, we observe that previous trackers generally ...
Learning task-specific discriminative representations for multiple object tracking ... object detection and multi-object tracking with graph neural networks.
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Learning Correlation for Online Multiple Object Tracking - IEEE Xplore
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In this paper, we follow the joint detection and tracking paradigm to learn correlation for online multiple object tracking. The proposed method, named LCTrack, ...
Oct 30, 2023 · We treat potential tracklets to be linked as a proposal and propose a trainable tracklet-to-proposal embedding framework based on graph attention network (GAT).
Mar 12, 2024 · Many works attempt the tracking by learning discriminative target representations, such as learning distractor-aware [48] or target-aware ...
Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks ...
Learning task-specific discriminative representations for multiple object tracking ... A novel learning framework for tracking multiple objects by detection ...
We propose a simple yet effective domain interaction training paradigm called domain contrast to boost discriminative object features.