Intelligent Video Traffic Monitoring

Chronic traffic jams cause long lines of vehicles to form at intersections. Long traffic jams are especially troublesome during rush hour. Vehicle activity, conflicting movement, and capacity are significantly affected by traffic signal systems at crossings. 

Traditional traffic signals employ rule-based controls, however, this strict methodology is ineffective in all traffic conditions. Moreover, the present sensors, which use induction loop technology implanted in the road surface, only give a general picture of the actual traffic condition.

Accurate traffic monitoring is required to comprehend vehicle movements and traffic flows around the city in order to produce a transportation system that ensures safety while also allowing for better traffic throughput.

The AI edge system outperforms conventional vehicle recognition techniques used for object tracking by performing traffic monitoring on enormous volumes of recorded video data using AI inference technologies. 

The edge AI system collects metadata from roads with deep learning computing and sends it to the central control room. The AI inference server, which is integrated with an easy-to-use dashboard in the control room, not only collects metadata from all edge AI devices but also keeps track of all traffic situations. The AI server can detect any anomalous circumstances and regulate the traffic lights accordingly.

The self-adaptive traffic signal control system ensures effective and streamlined traffic movement across the city with the analytical strength of the AI inference server. Information such as vehicle numbers, directions, waiting times, etc. can all easily be acquired using video equipped with edge-based AI systems.

All of this helps in the development of better transportation infrastructure through proactive transportation planning techniques like traffic effect analyses, public transportation, and road design.