Litedge

Abstract

Wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. However, cameras inside are generating a large amount of data, which brings challenges to the transmission through resource-constrained wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames can dramatically relieve the transmission pressure. Additionally, real-world environment may bring shielding or blind areas in videos, which notoriously affects the accuracy of frame analysis. The collaboration between cameras facing at different angles can compensate for such accuracy loss. In this work, we present Litedge, a light-weight edge computing strategy to improve the QoS (i. e., the latency and accuracy) of wireless surveillance systems. Two main modules are designed on edge cameras: (i) the light-weight video compression module for frame filtering, mainly realized by model compression and convolutional acceleration; and (ii) the collaborative validation module for error compensation between the master-slave camera pair. We also implement an enhanced surveillance system prototype from real-time monitoring and pre-processing on edge cameras to the backend data analysis on a server. Experiments based on real-world collected videos show the efficiency of Litedge. It achieves 82% transmission latency reduction with a maximal 0.119s additional processing delay, compared with the full video transmission. Remarkably, 91.28% of redundant frames are successfully filtered out, greatly reducing the transmission burden. Litedge outperforms state-of-the-art light-weight AI models and video compression methods by balancing the QoS balance ratio between accuracy and latency.