In this paper, we propose a new UAV video similarity measurement algorithm to cluster and analyze UAV videos by measuring the similarity between UAV videos and train them in sub-groups, which in turn improves the efficiency of UAV video data mining. Our preliminary work introduces the maximum common view subsequence (LCVS) to compute the similarity between UAV videos, but LCVS needs to frequently compute the common visible region of video frames generating extremely high time cost. In order to solve the problem of high computational cost of LCVS, we propose a similarity algorithm based on minimum tilted outer rectangle clustering (MTORC) for UAV videos. The MTORC algorithm adopts minimum tilted outer rectangle to cluster the field of view of the marked points in each frame, which reduces the time complexity of calculating the common visible region. By comparing experiments with LCSS and LCVS, the experimental results prove that MTORC is better than the present previous research in terms of algorithm accuracy and time cost.

The recent development of mobile and camera devices has led to the generation, sharing, and usage of massive amounts of video data. As a result, deep learning technology has gained attention as an alternative for video recognition and situation judgment. Recently, new systems supporting SQL-like declarative query languages have emerged, focusing on developing their own systems to support new queries combined with deep learning that are not supported by existing systems. The proposed DeepVQL system in this paper is implemented by expanding the PostgreSQL system. DeepVQL supports video database functions and provides various user-defined functions for object detection, object tracking, and video analytics queries. The advantage of this system is its ability to utilize queries with specific spatial regions or temporal durations as conditions for analyzing moving objects in traffic videos.