Computer Vision vs Crowdsourcing: Why Cameras Beat Apps for Incident Detection

Traffic incident detection has traditionally relied on two approaches: waiting for official reports from authorities, or crowdsourcing reports from drivers using apps like Waze. Both have the same fundamental problem—they depend on humans noticing, deciding to report, and then actually reporting an incident. Computer vision changes this equation entirely.

How Crowdsourcing Works (and Why It's Slow)

Waze pioneered crowdsourced traffic reporting. The premise is simple: millions of drivers on the road act as sensors, reporting incidents as they see them. In theory, this provides massive coverage without infrastructure investment.

In practice, here's what has to happen before a crowd-sourced incident appears:

By the time the incident appears, a half-mile queue has formed. Thousands of vehicles are already trapped.

How Computer Vision Works

Computer vision takes a fundamentally different approach: instead of waiting for humans to report, AI models watch traffic cameras directly and detect incidents the moment they occur.

Total time from incident to alert: under 10 seconds. That's 10-15 minutes before crowd-sourced apps even know something happened.

What Computer Vision Can Detect

Modern computer vision models trained for traffic monitoring can identify a wide range of incidents:

The Accuracy Question

Critics of computer vision often ask about false positives. It's a fair question—nobody wants alerts for non-incidents.

Modern computer vision achieves 95%+ accuracy for incident detection. Here's why:

• Multi-frame analysis: Models don't trigger on single frames—they analyze sequences to confirm incidents
• Contextual understanding: AI distinguishes between a vehicle stopped at a red light vs. stopped on a highway
• Severity classification: Not every detected event triggers a major alert—models assess severity
• Continuous learning: False positives improve the model through feedback loops

Compare this to crowdsourcing, where accuracy varies wildly based on user behavior, prank reports, and misidentified situations.

Coverage: The Real Differentiator

Crowdsourcing works well on busy routes where many Waze users drive. But what about:

• Rural highways with little traffic?
• Industrial areas at night?
• New road construction zones?
• Areas where users prefer other apps?

Computer vision works wherever cameras exist. Many DOTs and cities have extensive camera networks that remain underutilized for real-time incident detection. Argus taps into these networks, providing coverage regardless of how many app users are nearby.

The Verdict

Crowdsourcing was revolutionary when it launched. It proved that real-time traffic data could come from drivers themselves, not just expensive infrastructure.

But computer vision represents the next evolution. It's faster, more consistent, and doesn't depend on humans remembering to open an app and tap a button while driving.

For applications where speed matters—fleet operations, emergency response, navigation apps—computer vision isn't just better. It's transformative.