Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models

• URL: http://arxiv.org/abs/2606.09142v1
• Date: Mon, 08 Jun 2026 07:39:42 GMT
• Title: Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models
• Abstract summary: Egocentric vision offers a first-person view of human perception and decision making.<n>We study the decoding of pedestrian crossing intentions from short egocentric video clips.
• Score: 15.137239945564799
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored. In this work, we study the decoding of pedestrian crossing intentions from short egocentric video clips. We approach this by formulating the task as a closed-ended visual question answering (VQA) problem and leveraging vision language models (VLMs) to predict the pedestrians' intent. We first benchmark three families of state-of-the-art VLMs in a zero-shot setting, finding that they achieve moderate gains over random guessing but exhibit limited higher-level traffic reasoning. Motivated by these findings, we further adapt VLMs to the target task using parameter-efficient fine-tuning. Our results show that the fine-tuned models substantially outperform their zero-shot counterparts and achieve a 9\% accuracy improvement over a specialized transformer-based baseline. Finally, we demonstrate that incorporating additional contextual cues, including ego motion, vehicle motion, and eye gaze, further improves predictive performance. In particular, the fine-tuned Qwen3-VL-2B model guided by eye gaze and ego motion achieves a 14.5% accuracy improvement over the transformer baseline, establishing a new state of the art for egocentric pedestrian intent decoding.

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