Balancing robustness and information abundance via self-diagnosing in traffic surveillance video analysis

In this work, we propose a self-diagnosing intelligent highway surveillance system and design effective solutions different lighting and weather conditions. If tracking algorithms could work properly, performing tracking should be preferred in intelligent surveillance systems. However, it is unrealistic to segment and track each individual vehicle under all circumstances. Under congestion conditions, we propose a mechanism to estimate the traffic flow parameter via regression analysis. The experimental results have shown that the self-diagnosis ability and the modules designed for the system make the proposed system robust and reliable.