Fast highway abandoned object detection via block-based multi-group foreground extraction

Abandoned objects on highways pose a significant risk of causing severe traffic accidents. Current abandoned object detection technologies are limited by hardware constraints and real-time processing requirements in complex highway environments. To address these challenges, we propose a Universal foreground extraction framework consisting of Block-based frame selection and Multi-Group foreground Detection, called UBMG, which effectively alleviates false alarms caused by either illumination changes on the highway, traffic targets, or road markings. Specifically, two modules of block preprocessing and adaptive size filtering are first designed, followed by a static target matching module, to extract candidate targets. To accurately distinguish abandoned objects from other entities, a candidate verification strategy is proposed involving traffic target elimination, road noise elimination, and trajectory discrimination. This framework can seamlessly integrate with existing pixel-wise foreground detection algorithms and demonstrate high efficiency in practical applications. In addition, we have established a comprehensive video dataset of highway abandoned objects, named HAO, under various conditions (e.g.,lighting conditions, camera movements, and object occlusions) for thorough evaluation. Extensive experiments on HAO and public ABODA datasets demonstrate that the UBMG framework can perform robust and real-time detection of abandoned objects, especially outperforming the state-of-the-art methods in the HAO dataset and showing good performance on ABODA.