Challenges in Crosswalk Detection

Voice-over: ../assets/audio/challenges.mp3

Appearance Variability

Markings differ (zebra, ladder, continental) and paint degrades over time, reducing contrast. Non-standard designs and resurfaced roads further confuse simple pattern rules [1].

Left: faded crosswalk; Right: newly painted crosswalk — **Appearance variability:** faded markings (left) vs. fresh high-contrast paint (right). Source: 3M School Zone Safety Transformations, 2023.

Occlusion

Vehicles and crowds can partially cover stripes, hurting both edge-based methods and learned detectors [2].

Crowd crossing a zebra crosswalk in strong backlight — Pedestrians occlude stripes and scene context. Photo: Jacek Dylag / Unsplash.

Illumination & Weather

Harsh shadows, glare, rain/snow, and nighttime add noise and domain shift; low-light especially reduces stripe visibility [7].

Wet night street with headlights and low visibility — Night + rain: glare and reflections hide crosswalk texture. Photo: Alex Durynin / Unsplash.

Viewpoint & Scale

Onboard cameras view crosswalks at oblique angles and varying distances; perspective changes stripe geometry [2].

Long city avenue with many crosswalks seen at an oblique angle — Oblique viewpoint and distance compress stripe spacing. Photo: Tsuyoshi Kozu / Unsplash.

Domain Shift

Models trained in one city/camera setup may not generalize to another due to markings, pavement color, or intrinsics [7].

Aerial view of a diagonal multi-crosswalk intersection filled with pedestrians — Different cities use different crosswalk styles (e.g., diagonal scramble). Photo: Matthew Stephenson / Unsplash.

Beyond “Finding Paint”

Detection alone doesn’t answer whether it’s safe to cross. Multimodal methods can reason about signals, traffic, and pedestrians [3].

Pedestrian waiting near traffic signals at an intersection — Safety depends on context (signals, vehicles, pedestrians), not paint alone. Photo: Pieter de Boer / Unsplash.

References on this page: [1] [2] [3] [7]