LAUSR

In recent years, the Artificial Intelligence of Things (AIoT) has led to the rapid development of edge computing, and existing video-captioning systems can be deployed directly on AIoT-enabled cameras (hereafter referred to as edge cameras), which have increasingly powerful computing resources. Therefore, we propose a lightweight dense-video-captioning model based on the Transformer framework to improve execution efficiency for video-caption generation on edge cameras. In addition, to investigate the effect of concept drift on video captioning, we also propose an environment-aware adaptation to allow the system to respond to changes in the environment in order to produce more accurate video captions. Experimental results on the lightweight dense-video-captioning model show that the bilingual evaluation understudy (BLEU) metrics can increase by up to 23.5%, the computation time decreases by 46.4%, and the edge camera operates at a rate of 27.63 FPS, which is also faster than existing approaches by 4.7%. In addition, the mean average precision of the environment-aware adaptation is up to 11.3% higher than the existing approaches. In conclusion, the proposed approaches perform better than previous ones and are more flexible in different weather scenarios.