Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring
This work addresses the need for precise interior monitoring in autonomous vehicles, offering a more efficient solution, though it is incremental as it builds on existing multitask approaches in a specific domain.
The paper tackles the problem of vehicle occupancy monitoring by proposing a multitask network that jointly performs object detection, semantic segmentation, and human pose estimation, achieving higher accuracy while saving memory and computing costs compared to using separate networks.
In order to ensure safe autonomous driving, precise information about the conditions in and around the vehicle must be available. Accordingly, the monitoring of occupants and objects inside the vehicle is crucial. In the state-of-the-art, single or multiple deep neural networks are used for either object recognition, semantic segmentation, or human pose estimation. In contrast, we propose our Multitask Detection, Segmentation and Pose Estimation Network (MDSP) -- the first multitask network solving all these three tasks jointly in the area of occupancy monitoring. Due to the shared architecture, memory and computing costs can be saved while achieving higher accuracy. Furthermore, our architecture allows a flexible combination of the three mentioned tasks during a simple end-to-end training. We perform comprehensive evaluations on the public datasets SVIRO and TiCaM in order to demonstrate the superior performance.