论文标题
多任务网络用于车辆占用监测中的联合对象检测，语义细分和人姿势估计
Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring
论文作者
论文摘要
为了确保安全的自主驾驶，必须提供有关车辆内部和周围条件的精确信息。因此，对车辆内部的乘员和物体的监视至关重要。在最先进的情况下，单个或多个深层神经网络用于对象识别，语义分割或人姿势估计。相比之下，我们建议我们的多任务检测，分割和姿势估计网络（MDSP） - 在占用监控领域共同解决所有这三个任务的第一个多任务网络。由于共享体系结构，可以节省内存和计算成本，同时达到更高的准确性。此外，我们的体系结构可以在简单的端到端培训中灵活地组合提到的三个任务。我们在公共数据集SVIRO和TICAM上进行全面评估，以证明出色的性能。
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.
