Tracking many vehicles in wide coverage aerial imagery is crucial for understanding events in a large field of view. Most approaches aim to associate detections from frame differencing into tracks. However, slow or stopped vehicles result in long-term missing detections and further cause tracking discontinuities. Relying merely on appearance clue to recover missing detections is difficult as targets are extremely small and in grayscale. In this paper, we address the limitations of detection association methods by coupling it with a local context tracker (LCT), which does not rely on motion detections. On one hand, our LCT learns neighboring spatial relation and tracks each target in consecutive frames using graph optimization. It takes the advantage of context constraints to avoid drifting to nearby targets. We generate hypotheses from sparse and dense flow efficiently to keep solutions tractable. On the other hand, we use detection association strategy to extract short tracks in batch processing. We explicitly handle merged detections by generating additional hypotheses from them. Our evaluation on wide area aerial imagery sequences shows significant improvement over state-of-the-art methods.
Xiao al @cite_2 propose to use appearance and shape templates to handle missing detections. To avoid drifting, they use road network information and consider pairwise spatial relation in optimization. However, road network information is not always available and considering spatial relations in Hungarian optimization is costly. Basharat al @cite_4 apply an appearance-based tracker whenever detection association fails for a track or the motion of a target is slow. More recently, a hybrid approach that combines DBT with a regression-based tracker is proposed @cite_46 to handle stop-then-go vehicles. Nevertheless, relying only on weak appearance information makes these trackers prone to drift and limits its ability in recovering missing detections.
@cite_46 @cite_4 @cite_2
2004506571 2010593049
Persistent surveillance of large geographic areas from unmanned aerial vehicles allows us to learn much about the daily activities in the region of interest. Nearly all of the approaches addressing tracking in this imagery are detection-based and rely on background subtraction or frame differencing to provide detections. This, however, makes it difficult to track targets once they slow down or stop, which is not acceptable for persistent tracking, our goal. We present a multiple target tracking approach that does not exclusively rely on background subtraction and is better able to track targets through stops. It accomplishes this by effectively running two trackers in parallel: one based on detections from background subtraction providing target initialization and reacquisition, and one based on a target state regressor providing frame to frame tracking. We evaluated the proposed approach on a long sequence from a wide area aerial imagery dataset, and the results show improved object detection rates and ID-switch rates with limited increases in false alarms compared to the competition. We present a real-time, full-frame, multi-target Wide Area Motion Imagery (WAMI) tracking system that utilizes distributed processing to handle high data rates while maintaining high track quality. The proposed architecture processes the WAMI data as a series of geospatial tiles and implements both process- and thread-level parallelism across multiple compute nodes. Each tile is processed independently, from decoding the image through generating tracks that are finally merged across all tiles by an inter-tile linker (ITL) module. A high performance PostgreSQL database with GIS extensions is used to control the flow of intermediate data between each tracking process. High quality tracks are produced efficiently due to robust, effective algorithmic modules including: multi-frame moving object detection and track initialization; tracking based on the fusion of motion and appearance with a goal of very pure tracks; and online track linking based on multiple features. In addition, we have configured a high-performance compute cluster using high density blade servers, Infiniband networking, and an HPC filesystem. The compute cluster enables full-frame, state-of-the-art tracking of vehicles or dismounts at the WAMI sensor's native 1.25Hz frame-rate, while only taking 7u of rack space and providing 210 megapixels second throughput.