Multiple moving object segmentation using motion orientation histogram in adaptively partitioned blocks for high-resolution video surveillance systems

Abstract We present an efficient, robust moving object segmentation method that fully utilizes block motion information for high-resolution video surveillance systems. A high-resolution video surveillance system should satisfy two conflicting goals: (i) higher computational efficiency to manage the increasing amount of data and (ii) enhanced functionality in analyzing moving objects. In pursuit of both efficiency and functionality, we first quantize the orientation of motion vectors and then segment moving objects using adaptive block partitioning algorithm. We also present motion orientation histogram-based moving direction estimation. Major contribution of this work is the fully utilization of block motion information provided by either an image signal processing (ISP) chip or a digital signal processor (DSP) built-in software and the optimal representation of moving objects by the block divide-and-merge algorithm. Comparative experiments with the conventional video analysis algorithms show that the proposed method provides better segmentation results with the regular, efficient computational structure that can be easily embedded in an ISP chip or DSP software.