Label-based Multiple Object Ensemble Tracking with Randomized Frame Dropping
Abstract: This paper proposes a novel approach for ensemble tracking of multiple objects. Existing multiple object tracking methods often fail by appearance changes and occlusions of the targets, which lead to misdetection, mismatching, and drift of detections. The proposed method runs multiple "weak" trackers and aggregates the weak tracking results for each frame. Then, the proposed label-based ensemble is performed to track objects by considering a set of "weak" tracking results (instance IDs) for each target in a frame as a feature vector. This paper also proposes the randomized frame dropping that randomly drops input frames for each weak tracker to differentiate the inputs to the trackers. The proposed method can use any tracking algorithm as a weak tracker and pull up its performance. The performance of the proposed method has been confirmed by applying it to the current state-of-the-art method on MOT20, a public benchmark dataset.
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