Sliding Window Detection and Distance-Based Matching for Tracking on Gigapixel Images

Object detection and tracking are representative tasks in the field of computer vision. Existing methods have achieved commendable results on common datasets, yet they struggle to adapt to gigapixel images that demand higher spatio-temporal resolution and offer a greater spatial visibility range. In this paper, we propose a novel method for object detection and tracking dedicated designed for gigapixel images. Specifically: 1) We devise a multi-scale sliding window for object detection, effectively tackling the constraints of hardware conditions and the wide range of object scales present in the images; 2) We introduce a region proposal-based dense crowd detection algorithm within the sliding window, significantly enhancing the detection performance in crowded and occlusion-rich scenes; 3) We propose a distance-based strategy in the online tracking algorithm, enabling the tracker to maintain high tracking accuracy and identity consistency. The experimental results demonstrate that our proposed method significantly outperforms the baseline methods in terms of both detection and tracking performance.