Video Summarization for Object Tracking in the Internet of Things

Object tracking in the Internet of Things (IoT) has become a hot topic over the past ten years. Currently, the integration of video and radio-frequency identification (RFID) technology plays a crucial role in item-level activity recognition. Various techniques and applications have been proposed for visual object tracking. However, identifying semantic features of item-level objects in huge size of video content is a non-trivial task, especially in supply chain management. To alleviate this problem, this paper presents a novel method that applies IoT information to facilitate video summarization. Differing from common video summarization techniques, we use IoT information to select key frames of the video content during the background model establishment. Then we match other key frames with the background to extract important features. Finally, a compact summarization image for queried objects is generated according to a clustering analysis. We have also performed experiments to confirm the effectiveness of the proposed work.