A Realtime Framework for Video Object Detection with Storm

Real-time response is a challenging issue for video object detection, especially when the number of cameras is large and correspondingly the video data are big. The existing solutions for object detection fall short in addressing the real-time performance aspect, and can not handle fast response requirements such as fleeing vehicle tracking at run time. Therefore, in this paper we propose a Storm-based real-time framework for video object detection that can scale to handle large number of cameras. To evaluate its performance, we implement the framework in a Storm cluster environment where we test the detection rate and real-time performance of the framework. The results show that the detection rate is relatively acceptable and real-time response is achieved.