# Real-time Intent Prediction of Pedestrians for Autonomous Ground Vehicles via Spatio-Temporal DenseNet

## TL;DR

This paper presents a real-time framework using a novel spatio-temporal DenseNet model to accurately predict pedestrian intentions in urban traffic scenes from monocular camera images, enhancing autonomous vehicle safety.

## Contribution

The paper introduces a new spatio-temporal DenseNet model combined with a tracking-by-detection approach for real-time pedestrian intent prediction in urban environments.

## Key findings

- Achieved 84.76% average precision in intent prediction.
- Operates at 20 frames per second in real-time.
- Outperforms baseline methods in accuracy and speed.

## Abstract

Understanding the behaviors and intentions of humans are one of the main challenges autonomous ground vehicles still faced with. More specifically, when it comes to complex environments such as urban traffic scenes, inferring the intentions and actions of vulnerable road users such as pedestrians become even harder. In this paper, we address the problem of intent action prediction of pedestrians in urban traffic environments using only image sequences from a monocular RGB camera. We propose a real-time framework that can accurately detect, track and predict the intended actions of pedestrians based on a tracking-by-detection technique in conjunction with a novel spatio-temporal DenseNet model. We trained and evaluated our framework based on real data collected from urban traffic environments. Our framework has shown resilient and competitive results in comparison to other baseline approaches. Overall, we achieved an average precision score of 84.76% with a real-time performance at 20 FPS.