Local and Global Collaboration for Object Detection Enhancement with Information Redundancy

Object detection by visual sensors is a critical component of surveillance systems and has many challenging issues. This paper addresses enhancement of object detection with multiple visual sensors. The detection enhancement we introduce is to recover missed object detection given partially detected objects among multiple visual sensors. Once an object is detected by one or more visual sensors, the detected local object positions are transformed into a global object position. Based on a local and global collaboration, any missed local object position is recovered by the global to local transformation. However, the collaboration may degrade the detection performance by incorrectly recovering the local object position, which is propagated from false object detection. Furthermore, local object positions corresponding to an identical object are transformed into in equivalent global object positions due to detection uncertainty such as a shadow. In this paper, we minimize the performance degradation by preventing from the propagation of the false object detection. In addition, we present an evaluation method for a final global object position. Finally, the proposed method is analyzed and evaluated with case studies.