Enhanced Parking Perception by Multi-Task Fisheye Cross-view Transformers

Current parking area perception algorithms primarily focus on detecting vacant slots within a limited range, relying on error-prone homographic projection for both labeling and inference.

However, recent advancements in Advanced Driver Assistance System (ADAS) require interaction with end-users through comprehensive and intelligent Human-Machine Interfaces (HMIs).

These interfaces should present a complete perception of the parking area going from distinguishing vacant slots' entry lines to the orientation of other parked vehicles.

This paper introduces Multi-Task Fisheye Cross View Transformers (MT F-CVT), which leverages features from a four-camera fisheye Surround-view Camera System (SVCS) with multihead attentions to create a detailed Bird-Eye View (BEV) grid feature map.

Features are processed by both a segmentation decoder and a Polygon-Yolo based object detection decoder for parking slots and vehicles.

Trained on data labeled using LiDAR, MT F-CVT positions objects within a 25m x 25m real open-road scenes with an average error of only 20 cm.

Our larger model achieves an F-1 score of 0.89.

Moreover the smaller model operates at 16 fps on an Nvidia Jetson Orin embedded board, with similar detection results to the larger one.

MT F-CVT demonstrates robust generalization capability across different vehicles and camera rig configurations.

A demo video from an unseen vehicle and camera rig is available at: https://streamable.com/jjw54x.

;Comment: This paper is a preprint of a paper submitted to the 26th Irish Machine Vision and Image Processing Conference (IMVIP 2024).

If accepted, the copy of record will be available at IET Digital Library