Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking
| Autor: | Van Ma, Linh, Hussain, Muhammad Ishfaq, Park, JongHyun, Kim, Jeongbae, Jeon, Moongu |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | The 12th International Conference on Control, Automation and Information Sciences (ICCAIS 2023) |
| Druh dokumentu: | Working Paper |
| Popis: | We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack. Comment: The 12th International Conference on Control, Automation and Information Sciences (ICCAIS 2023), November 27th to 29th, 2023 in Hanoi |
| Databáze: | arXiv |
| Externí odkaz: |
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