An Improved Interaction Estimation and Optimization Method for Surveillance Video Synopsis

Videos synopsis is an efficient technique for condensing long-duration videos into short videos. The interactions between moving objects in the original video need to be preserved during video condensation. However, identifying objects with strong spatio-temporal proximity from a monocular video frame is a challenge. Further, the process of tube rearrangement optimization is also vital for the reduction of collision rates among moving objects. Taking the aforementioned aspects into consideration, we present a comprehensive video synopsis framework. First, we propose an interaction detection method to estimate distortion less spatio-temporal interactions between moving objects by generating the top view of a scene using a perspective transformation. Second, we propose an optimization method to reduce collisions and preserve object interactions by shrinking the search space. The experimental results demonstrate that the proposed framework provides a better estimate for object interactions from surveillance videos and generates synopsis videos with fewer collisions while preserving original interactions.