Razali, HaziqMordan, TaylorAlahi, Alexandre2021-06-142021-06-142021-06-142021-07-1510.1016/j.trc.2021.103259https://infoscience.epfl.ch/handle/20.500.14299/178843The ability to predict pedestrian behaviour is crucial for road safety, traffic management systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous vehicles. We present a vision-based system that simultaneously locates where pedestrians are in the scene, estimates their body pose and predicts their intention to cross the road. Given a single image, our proposed neural network is designed using a bottom-up approach and thus runs at nearly constant time without relying on a pedestrian detector. Our method jointly detects human body poses and predicts their intention in a multitask framework. Experimental results show that the proposed model outperforms the precision scores of the state-of-the-art for the task of intention prediction by approximately 20% while running in real-time (5 fps). The source code is publicly available so that it can be easily integrated into an ADAS or into any traffic light management systems.Traffic Management SystemsAdvanced Driver Assistance SystemsAutonomous VehiclesPedestrian Intention PredictionHuman Pose EstimationHuman Behaviour Analysistext::journal::journal article::research article