Single Shot Multitask Pedestrian Detection and Behavior Prediction
This work is significant for self-driving vehicle manufacturers and developers, offering an incremental improvement in the speed and accuracy of pedestrian interaction prediction.
This paper addresses the critical need for fast and memory-efficient pedestrian detection and behavior prediction in self-driving vehicles. The proposed architecture significantly reduces latency by performing detection and intention prediction in a single shot, while also improving accuracy through feature sharing.
Detecting and predicting the behavior of pedestrians is extremely crucial for self-driving vehicles to plan and interact with them safely. Although there have been several research works in this area, it is important to have fast and memory efficient models such that it can operate in embedded hardware in these autonomous machines. In this work, we propose a novel architecture using spatial-temporal multi-tasking to do camera based pedestrian detection and intention prediction. Our approach significantly reduces the latency by being able to detect and predict all pedestrians' intention in a single shot manner while also being able to attain better accuracy by sharing features with relevant object level information and interactions.