Single Shot Multitask Pedestrian Detection and Behavior Prediction
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 6 Jan 2021.
Builds on
16 of 24 references.
References · 16
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4 more
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Citing papers per year: 2023: 1.
1 paper.