Aerial Multi-object Tracking via Information Weighting

Multi-object tracking from an aerial perspective often faces typical challenges such as small objects, dual-source motion, and appearance similarity. This often results in low tracking accuracy. In this paper, we propose an Aerial multi-object Tracking method via Information Weighting (ATIW), which comprises four main components: adaptive weighting, distribution feature extraction, prediction box correction, and spatiotemporal feature enhancement. Adaptive weighting involves a dynamic fusion of distribution, motion, and appearance information, tailored to the object's scale and velocity. Distribution feature extraction utilizes the spatial distribution information of objects and their neighbors to facilitate identity association. The purpose of prediction box correction is to mitigate the negative impact of camera rotation on IoU matching. Spatiotemporal feature enhancement aims to fuse corresponding features based on the object's feature similarity in adjacent frames and the object's unique feature differences. The experimental results from the UAVDT dataset demonstrate that the proposed method can effectively improve the performance of aerial multi-object tracking. In particular, the IDF1 metric is improved by 0.5% without retraining the model.