CECVA: Communication-Efficient Edge-Device Collaborative Video Analysis

In recent years, the rapid development of artificial intelligence and deep learning technologies has significantly advanced video analysis techniques in smart cities and their related applications, extensively used in vehicle detection, behavior recognition, and event classification. However, traditional video analysis systems typically transfer large volumes of raw data from edge devices directly to cloud servers for processing, a practice that not only incurs high communication costs but also results in significant inference delays. To address these challenges, we propose a communication-efficient edge-to-device collaborative video analysis system. This system integrates multiple edge cameras with an edge server to perform multi-camera pedestrian occupancy prediction tasks. The system efficiently extracts task-relevant features on edge devices based on the information bottleneck principle. It employs an entropy coding scheme for feature compression and transmission, significantly reducing communication costs while ensuring inference performance. Additionally, we have designed a lightweight feature extractor suitable for resource-constrained edge devices. On the edge server side, we proposed a view contribution weighting module that effectively integrates spatial information from multiple cameras and leverages historical data to enhance current inference. Through spatio-temporal information fusion, we have significantly improved the system's prediction accuracy and efficiency. Experimental results indicate that our approach achieves a better trade-off between reducing communication overhead and maintaining high performance.