Multi object tracking (MOT) in UAV imagery is challenged by weak feature representation of small objects due to limited resolution, which leads to frequent missed detections. However, enhancing small object features often amplifies background noise and increases false positives. To address this contradiction, we propose the Temporal Aware Small Object Enhancement Network (TASONet), which integrates spatial enhancement and temporal modeling for robust tracking. The Small Object Enhancement (SOE) module combines depthwise separable convolutions with contrast-aware attention mechanisms (SimAM and LCDAttn) to improve local discriminability. It further incorporates the Small Target Enhancement Path (STEP), which uses motion-difference cues and a confidence adaptive suppression strategy to strengthen spatial features while mitigating noise. The Temporal Enhancement Module (TEM), consisting of Temporal Feature Alignment (TFA) and a Target Memory Unit (TMU), aggregates multi-frame information through adaptive inter-frame fusion and memory of high confidence historical features, improving temporal consistency and reducing false positives potentially introduced by SOE. Experiments show that TASONet achieves significant gains over state-of-the-art methods: on UAVDT, MOTA increases from 68.33 to 75.97 and IDF1 from 83.50 to 88.51; on VisDrone-MOT, MOTA rises from 61.15 to 73.52 with an IDF1 of 88.83. These results validate the effectiveness of jointly enhancing spatial features and temporal coherence for UAV small-object MOT.
Ma et al. (Wed,) studied this question.