Abstract: The present invention generally relates to a technology of detecting a stationary object in a static video, i.e., a video which is generated by a camera with a fixed view, such as a CCTV video. More specifically, the present invention relates to a technology of identifying a stationary object by use of inference information (Tensor, Activation Map) of Deep Learning object detector among objects which have been detected in a static video such as a CCTV video. According to the present invention, it is possible to lower the priority of stationary objects (e.g., parked vehicles) in processing a static video so that computing resource of the video analysis system can be effectively utilized in the process after object detection so as to enhance the performance of image analysis.

Abstract: The present invention generally relates to a technology of detecting a stationary object in a static video, i.e., a video which is generated by a camera with a fixed view, such as a CCTV video. More specifically, the present invention relates to a technology of identifying a stationary object by use of inference information (Tensor, Activation Map) of Deep Learning object detector among objects which have been detected in a static video such as a CCTV video. According to the present invention, it is possible to lower the priority of stationary objects (e.g., parked vehicles) in processing a static video so that computing resource of the video analysis system can be effectively utilized in the process after object detection so as to enhance the performance of image analysis.