Ranked Window Query Retrieval over Video Repositories

Recent advances in Computer Vision have contributed to solid accuracy and efficiency improvements in many tasks such as object detection and tracking, enabling new opportunities for video analytics. In this paper, we initiate the study of ranked window queries that aim to retrieve clips from large video repositories in which objects co-occur in a query-specified fashion. For example, ranked window queries allow retrieval of clips of a set duration (e.g., 10 seconds) with the highest score from a long video, where at least the same 3 cars (with matching conditions based on suitably defined metadata) appear jointly. To answer such queries, we propose a two-phased approach, which builds indexes for all desired objects of the given videos during an Ingestion Phase and evaluates query answers efficiently in the Query Phase. During the Ingestion Phase, the proposed Partition-Based Index Construction (PBIC) algorithm builds indexes on partitions obtained by splitting each given video. Leveraging such indexes, queries are answered in the Query Phase using the Partition-Based Query Processing (PBQP) algorithm, which efficiently produces the desired (query-specified) number of results with the highest scores. We present the outcome of a thorough performance study on real videos that evaluates the performance of the proposed algorithms by varying parameters of interest. Our results indicate that the proposed set of techniques are capable of processing queries efficiently at scale, demonstrating multiple orders of magnitude speedups over other applicable approaches.