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About this paper
Zhewen Zhang, Fuliang Wu, Yuming Qiu, Jingdong Liang, Shuiwang Li
Guilin University of Technology researchers introduce TSFMO, the first dedicated benchmark dataset for tracking small and fast-moving objects, featuring targets significantly smaller and faster than in existing benchmarks. They also propose S-KeepTrack, a modified tracker that effectively fuses low-level and high-level features, achieving an Area Under Curve (AUC) of 0.255 on TSFMO.
Key insights
- State-of-the-art trackers exhibit significantly reduced performance on TSFMO compared to generic benchmarks, indicating their inability to generalize effectively to the combined challenges of small and fast-moving targets.
- The fusion of low-level and high-level features is critical for robustly tracking small and fast objects, as demonstrated by S-KeepTrack's superior performance.
- Deeper backbone networks (e.g., ResNet-50) consistently improve tracking performance across various algorithms, emphasizing the importance of learning strong feature representations even for small targets.
Summary by alphaXiv.
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